{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.set_autosave_interval(600000)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autosaving every 600 seconds\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from collections import OrderedDict, namedtuple\n",
    "import numpy as np\n",
    "from numpy import linspace\n",
    "from pandas import DataFrame\n",
    "from scipy.integrate import odeint, ode\n",
    "import ggplot as gg\n",
    "%autosave 600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HAS_SOLVEIVP = False\n",
    "try:\n",
    "    from scipy.integrate import solve_ivp\n",
    "    HAS_SOLVEIVP = True\n",
    "except:\n",
    "    pass\n",
    "if not HAS_SOLVEIVP:\n",
    "    try:\n",
    "        from scipy_ode import solve_ivp\n",
    "        HAS_SOLVEIVP = True\n",
    "    except:\n",
    "        pass\n",
    "HAS_SOLVEIVP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HAS_ODES = False\n",
    "try:\n",
    "    from scikits.odes.odeint import odeint as odes_odeint\n",
    "    from scikits.odes import ode as odes_ode\n",
    "    HAS_ODES = True\n",
    "except:\n",
    "    pass\n",
    "HAS_ODES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models to use in performance test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class egfngf_model:\n",
    "    def __init__(self):\n",
    "        self.name = 'egfngf'\n",
    "        self.ts = linspace(0, 120, 121, dtype=float)\n",
    "        self.has_userdata = True\n",
    "        self.has_userdata_odes = True\n",
    "        self.k = [\n",
    "                    2.18503E-5,\n",
    "                    0.0121008,\n",
    "                    1.38209E-7,\n",
    "                    0.00723811,\n",
    "                    694.731,\n",
    "                    6086070.0,\n",
    "                    389.428,\n",
    "                    2112.66,\n",
    "                    1611.97,\n",
    "                    896896.0,\n",
    "                    32.344,\n",
    "                    35954.3,\n",
    "                    1509.36,\n",
    "                    1432410.0,\n",
    "                    0.884096,\n",
    "                    62464.6,\n",
    "                    185.759,\n",
    "                    4768350.0,\n",
    "                    125.089,\n",
    "                    157948.0,\n",
    "                    2.83243,\n",
    "                    518753.0,\n",
    "                    9.85367,\n",
    "                    1007340.0,\n",
    "                    8.8912,\n",
    "                    3496490.0,\n",
    "                    0.0213697,\n",
    "                    763523.0,\n",
    "                    10.6737,\n",
    "                    184912.0,\n",
    "                    0.0771067,\n",
    "                    272056.0,\n",
    "                    0.0566279,\n",
    "                    653951.0,\n",
    "                    15.1212,\n",
    "                    119355.0,\n",
    "                    146.912,\n",
    "                    12876.2,\n",
    "                    1.40145,\n",
    "                    10965.6,\n",
    "                    27.265,\n",
    "                    295990.0,\n",
    "                    2.20995,\n",
    "                    1025460.0,\n",
    "                    0.126329,\n",
    "                    1061.71,\n",
    "                    441.287,\n",
    "                    1.08795E7\n",
    "                ]\n",
    "        \n",
    "        self.userdata = self.k\n",
    "\n",
    "        self.y0 = [\n",
    "                    1000,\n",
    "                    4560,\n",
    "                    80000.0,\n",
    "                    0.0,\n",
    "                    10000.0,\n",
    "                    0.0,\n",
    "                    120000.0,\n",
    "                    0.0,\n",
    "                    120000.0,\n",
    "                    0.0,\n",
    "                    120000.0,\n",
    "                    0.0,\n",
    "                    120000.0,\n",
    "                    0.0,\n",
    "                    120000.0,\n",
    "                    0.0,\n",
    "                    600000.0,\n",
    "                    0.0,\n",
    "                    600000.0,\n",
    "                    0.0,\n",
    "                    120000.0,\n",
    "                    0.0,\n",
    "                    120000.0,\n",
    "                    0.0,\n",
    "                    120000.0,\n",
    "                    0.0,\n",
    "                    120000.0,\n",
    "                    0.0,\n",
    "                    120000.0,\n",
    "                    120000.0,\n",
    "                    120000.0,\n",
    "                    120000.0\n",
    "                ]\n",
    "\n",
    "    def f(self, t, y, k):\n",
    "        return [\n",
    "            ((-1.0 * k[0] * y[0] * y[2])) + (1.0 * k[1] * y[3]),\n",
    "            ((-1.0 * k[2] * y[1] * y[4])) + (1.0 * k[3] * y[5]),\n",
    "            ((-1.0 * k[0] * y[0] * y[2])) + (1.0 * k[1] * y[3]),\n",
    "            ((1.0 * k[0] * y[0] * y[2]) + (-1.0 * k[1] * y[3])),\n",
    "            ((-1.0 * k[2] * y[1] * y[4]) + (1.0 * k[3] * y[5])),\n",
    "            ((1.0 * k[2] * y[1] * y[4]) + (-1.0 * k[3] * y[5])),\n",
    "            ((-1.0 * k[4] * y[3] * y[6] / (y[6] + k[5])) + (-1.0 * k[6] * y[5] * y[6] / (y[6] + k[7])) + (\n",
    "                1.0 * k[8] * y[9] * y[7] / (y[7] + k[9]))),\n",
    "            ((1.0 * k[4] * y[3] * y[6] / (y[6] + k[5])) + (1.0 * k[6] * y[5] * y[6] / (y[6] + k[7])) + (\n",
    "                -1.0 * k[8] * y[9] * y[7] / (y[7] + k[9]))),\n",
    "            ((-1.0 * k[26] * y[19] * y[8] / (y[8] + k[27]))),\n",
    "            ((1.0 * k[26] * y[19] * y[8] / (y[8] + k[27]))),\n",
    "            ((-1.0 * k[10] * y[7] * y[10] / (y[10] + k[11])) + (1.0 * k[12] * y[28] * y[11] / (y[11] + k[13]))),\n",
    "            ((1.0 * k[10] * y[7] * y[10] / (y[10] + k[11])) + (-1.0 * k[12] * y[28] * y[11] / (y[11] + k[13]))),\n",
    "            ((-1.0 * k[14] * y[11] * y[12] / (y[12] + k[15])) + (1.0 * k[44] * y[31] * y[13] / (y[13] + k[45])) + (\n",
    "                1.0 * k[34] * y[23] * y[13] / (y[13] + k[35]))),\n",
    "            ((1.0 * k[14] * y[11] * y[12] / (y[12] + k[15])) + (-1.0 * k[44] * y[31] * y[13] / (y[13] + k[45])) + (\n",
    "                -1.0 * k[34] * y[23] * y[13] / (y[13] + k[35]))),\n",
    "            ((-1.0 * k[42] * y[27] * y[14] / (y[14] + k[43])) + (1.0 * k[46] * y[31] * y[15] / (y[15] + k[47]))),\n",
    "            ((1.0 * k[42] * y[27] * y[14] / (y[14] + k[43])) + (-1.0 * k[46] * y[31] * y[15] / (y[15] + k[47]))),\n",
    "            ((-1.0 * k[16] * y[13] * y[16] / (y[16] + k[17])) + (-1.0 * k[18] * y[15] * y[16] / (y[16] + k[19])) + (\n",
    "                1.0 * k[20] * y[30] * y[17] / (y[17] + k[21]))),\n",
    "            ((1.0 * k[16] * y[13] * y[16] / (y[16] + k[17])) + (1.0 * k[18] * y[15] * y[16] / (y[16] + k[19])) + (\n",
    "                -1.0 * k[20] * y[30] * y[17] / (y[17] + k[21]))),\n",
    "            ((-1.0 * k[22] * y[17] * y[18] / (y[18] + k[23])) + (1.0 * k[24] * y[30] * y[19] / (y[19] + k[25]))),\n",
    "            ((1.0 * k[22] * y[17] * y[18] / (y[18] + k[23])) + (-1.0 * k[24] * y[30] * y[19] / (y[19] + k[25]))),\n",
    "            ((-1.0 * k[28] * y[3] * y[20] / (y[20] + k[29])) + (-1.0 * k[30] * y[11] * y[20] / (y[20] + k[31]))),\n",
    "            ((1.0 * k[28] * y[3] * y[20] / (y[20] + k[29])) + (1.0 * k[30] * y[11] * y[20] / (y[20] + k[31]))),\n",
    "            ((-1.0 * k[32] * y[21] * y[22] / (y[22] + k[33]))),\n",
    "            ((1.0 * k[32] * y[21] * y[22] / (y[22] + k[33]))),\n",
    "            ((-1.0 * k[36] * y[5] * y[24] / (y[24] + k[37]))),\n",
    "            ((1.0 * k[36] * y[5] * y[24] / (y[24] + k[37]))),\n",
    "            ((-1.0 * k[38] * y[25] * y[26] / (y[26] + k[39])) + (1.0 * k[40] * y[29] * y[27] / (y[27] + k[41]))),\n",
    "            ((1.0 * k[38] * y[25] * y[26] / (y[26] + k[39])) + (-1.0 * k[40] * y[29] * y[27] / (y[27] + k[41]))),\n",
    "            0,\n",
    "            0,\n",
    "            0,\n",
    "            0\n",
    "        ]\n",
    "\n",
    "\n",
    "    def f_odes(self, t, y, yout, k):\n",
    "        yout[:] = [\n",
    "            ((-1.0 * k[0] * y[0] * y[2])) + (1.0 * k[1] * y[3]),\n",
    "            ((-1.0 * k[2] * y[1] * y[4])) + (1.0 * k[3] * y[5]),\n",
    "            ((-1.0 * k[0] * y[0] * y[2])) + (1.0 * k[1] * y[3]),\n",
    "            ((1.0 * k[0] * y[0] * y[2]) + (-1.0 * k[1] * y[3])),\n",
    "            ((-1.0 * k[2] * y[1] * y[4]) + (1.0 * k[3] * y[5])),\n",
    "            ((1.0 * k[2] * y[1] * y[4]) + (-1.0 * k[3] * y[5])),\n",
    "            ((-1.0 * k[4] * y[3] * y[6] / (y[6] + k[5])) + (-1.0 * k[6] * y[5] * y[6] / (y[6] + k[7])) + (\n",
    "                1.0 * k[8] * y[9] * y[7] / (y[7] + k[9]))),\n",
    "            ((1.0 * k[4] * y[3] * y[6] / (y[6] + k[5])) + (1.0 * k[6] * y[5] * y[6] / (y[6] + k[7])) + (\n",
    "                -1.0 * k[8] * y[9] * y[7] / (y[7] + k[9]))),\n",
    "            ((-1.0 * k[26] * y[19] * y[8] / (y[8] + k[27]))),\n",
    "            ((1.0 * k[26] * y[19] * y[8] / (y[8] + k[27]))),\n",
    "            ((-1.0 * k[10] * y[7] * y[10] / (y[10] + k[11])) + (1.0 * k[12] * y[28] * y[11] / (y[11] + k[13]))),\n",
    "            ((1.0 * k[10] * y[7] * y[10] / (y[10] + k[11])) + (-1.0 * k[12] * y[28] * y[11] / (y[11] + k[13]))),\n",
    "            ((-1.0 * k[14] * y[11] * y[12] / (y[12] + k[15])) + (1.0 * k[44] * y[31] * y[13] / (y[13] + k[45])) + (\n",
    "                1.0 * k[34] * y[23] * y[13] / (y[13] + k[35]))),\n",
    "            ((1.0 * k[14] * y[11] * y[12] / (y[12] + k[15])) + (-1.0 * k[44] * y[31] * y[13] / (y[13] + k[45])) + (\n",
    "                -1.0 * k[34] * y[23] * y[13] / (y[13] + k[35]))),\n",
    "            ((-1.0 * k[42] * y[27] * y[14] / (y[14] + k[43])) + (1.0 * k[46] * y[31] * y[15] / (y[15] + k[47]))),\n",
    "            ((1.0 * k[42] * y[27] * y[14] / (y[14] + k[43])) + (-1.0 * k[46] * y[31] * y[15] / (y[15] + k[47]))),\n",
    "            ((-1.0 * k[16] * y[13] * y[16] / (y[16] + k[17])) + (-1.0 * k[18] * y[15] * y[16] / (y[16] + k[19])) + (\n",
    "                1.0 * k[20] * y[30] * y[17] / (y[17] + k[21]))),\n",
    "            ((1.0 * k[16] * y[13] * y[16] / (y[16] + k[17])) + (1.0 * k[18] * y[15] * y[16] / (y[16] + k[19])) + (\n",
    "                -1.0 * k[20] * y[30] * y[17] / (y[17] + k[21]))),\n",
    "            ((-1.0 * k[22] * y[17] * y[18] / (y[18] + k[23])) + (1.0 * k[24] * y[30] * y[19] / (y[19] + k[25]))),\n",
    "            ((1.0 * k[22] * y[17] * y[18] / (y[18] + k[23])) + (-1.0 * k[24] * y[30] * y[19] / (y[19] + k[25]))),\n",
    "            ((-1.0 * k[28] * y[3] * y[20] / (y[20] + k[29])) + (-1.0 * k[30] * y[11] * y[20] / (y[20] + k[31]))),\n",
    "            ((1.0 * k[28] * y[3] * y[20] / (y[20] + k[29])) + (1.0 * k[30] * y[11] * y[20] / (y[20] + k[31]))),\n",
    "            ((-1.0 * k[32] * y[21] * y[22] / (y[22] + k[33]))),\n",
    "            ((1.0 * k[32] * y[21] * y[22] / (y[22] + k[33]))),\n",
    "            ((-1.0 * k[36] * y[5] * y[24] / (y[24] + k[37]))),\n",
    "            ((1.0 * k[36] * y[5] * y[24] / (y[24] + k[37]))),\n",
    "            ((-1.0 * k[38] * y[25] * y[26] / (y[26] + k[39])) + (1.0 * k[40] * y[29] * y[27] / (y[27] + k[41]))),\n",
    "            ((1.0 * k[38] * y[25] * y[26] / (y[26] + k[39])) + (-1.0 * k[40] * y[29] * y[27] / (y[27] + k[41]))),\n",
    "            0,\n",
    "            0,\n",
    "            0,\n",
    "            0\n",
    "        ]\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/Cython/Distutils/old_build_ext.py:30: UserWarning: Cython.Distutils.old_build_ext does not properly handle dependencies and is deprecated.\n",
      "  \"Cython.Distutils.old_build_ext does not properly handle dependencies \"\n"
     ]
    }
   ],
   "source": [
    "%load_ext Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%cython -I /home/benny/git/odes/scikits/odes/sundials/ -I /usr/local/lib/python3.5/dist-packages/scikits.odes-2.3.0.dev0-py3.5-linux-x86_64.egg/scikits/odes/sundials/\n",
    "## update include flag -I to point to odes/sundials directory!\n",
    "import numpy as np\n",
    "from cpython cimport bool\n",
    "cimport numpy as np\n",
    "from scikits.odes.sundials.cvode cimport CV_RhsFunction\n",
    "\n",
    "#scikits.odes allows cython functions only if derived from correct class\n",
    "\n",
    "cdef class egfngf_cython_model(CV_RhsFunction):\n",
    "    cdef public ts, k, y0, userdata\n",
    "    cdef public object name\n",
    "    cdef public CV_RhsFunction f_odes\n",
    "    cdef public bool has_userdata, has_userdata_odes\n",
    "    \n",
    "    def __cinit__(self):\n",
    "        self.name = 'egfngf_cython'\n",
    "        self.ts = np.linspace(0, 120, 121, dtype=float)\n",
    "        self.has_userdata = True\n",
    "        self.has_userdata_odes = True\n",
    "        self.k = np.array([\n",
    "                    2.18503E-5,\n",
    "                    0.0121008,\n",
    "                    1.38209E-7,\n",
    "                    0.00723811,\n",
    "                    694.731,\n",
    "                    6086070.0,\n",
    "                    389.428,\n",
    "                    2112.66,\n",
    "                    1611.97,\n",
    "                    896896.0,\n",
    "                    32.344,\n",
    "                    35954.3,\n",
    "                    1509.36,\n",
    "                    1432410.0,\n",
    "                    0.884096,\n",
    "                    62464.6,\n",
    "                    185.759,\n",
    "                    4768350.0,\n",
    "                    125.089,\n",
    "                    157948.0,\n",
    "                    2.83243,\n",
    "                    518753.0,\n",
    "                    9.85367,\n",
    "                    1007340.0,\n",
    "                    8.8912,\n",
    "                    3496490.0,\n",
    "                    0.0213697,\n",
    "                    763523.0,\n",
    "                    10.6737,\n",
    "                    184912.0,\n",
    "                    0.0771067,\n",
    "                    272056.0,\n",
    "                    0.0566279,\n",
    "                    653951.0,\n",
    "                    15.1212,\n",
    "                    119355.0,\n",
    "                    146.912,\n",
    "                    12876.2,\n",
    "                    1.40145,\n",
    "                    10965.6,\n",
    "                    27.265,\n",
    "                    295990.0,\n",
    "                    2.20995,\n",
    "                    1025460.0,\n",
    "                    0.126329,\n",
    "                    1061.71,\n",
    "                    441.287,\n",
    "                    1.08795E7\n",
    "                ], float)\n",
    "        \n",
    "        self.userdata = self.k\n",
    "\n",
    "        self.y0 = np.array([\n",
    "                    1000,\n",
    "                    4560,\n",
    "                    80000.0,\n",
    "                    0.0,\n",
    "                    10000.0,\n",
    "                    0.0,\n",
    "                    120000.0,\n",
    "                    0.0,\n",
    "                    120000.0,\n",
    "                    0.0,\n",
    "                    120000.0,\n",
    "                    0.0,\n",
    "                    120000.0,\n",
    "                    0.0,\n",
    "                    120000.0,\n",
    "                    0.0,\n",
    "                    600000.0,\n",
    "                    0.0,\n",
    "                    600000.0,\n",
    "                    0.0,\n",
    "                    120000.0,\n",
    "                    0.0,\n",
    "                    120000.0,\n",
    "                    0.0,\n",
    "                    120000.0,\n",
    "                    0.0,\n",
    "                    120000.0,\n",
    "                    0.0,\n",
    "                    120000.0,\n",
    "                    120000.0,\n",
    "                    120000.0,\n",
    "                    120000.0\n",
    "                ], float)\n",
    "\n",
    "    cpdef np.ndarray[double, ndim=1] f(self, double t, np.ndarray[double, ndim=1] y, \n",
    "                                       np.ndarray[double, ndim=1] k):\n",
    "        return np.array([\n",
    "            ((-1.0 * k[0] * y[0] * y[2])) + (1.0 * k[1] * y[3]),\n",
    "            ((-1.0 * k[2] * y[1] * y[4])) + (1.0 * k[3] * y[5]),\n",
    "            ((-1.0 * k[0] * y[0] * y[2])) + (1.0 * k[1] * y[3]),\n",
    "            ((1.0 * k[0] * y[0] * y[2]) + (-1.0 * k[1] * y[3])),\n",
    "            ((-1.0 * k[2] * y[1] * y[4]) + (1.0 * k[3] * y[5])),\n",
    "            ((1.0 * k[2] * y[1] * y[4]) + (-1.0 * k[3] * y[5])),\n",
    "            ((-1.0 * k[4] * y[3] * y[6] / (y[6] + k[5])) + (-1.0 * k[6] * y[5] * y[6] / (y[6] + k[7])) + (\n",
    "                1.0 * k[8] * y[9] * y[7] / (y[7] + k[9]))),\n",
    "            ((1.0 * k[4] * y[3] * y[6] / (y[6] + k[5])) + (1.0 * k[6] * y[5] * y[6] / (y[6] + k[7])) + (\n",
    "                -1.0 * k[8] * y[9] * y[7] / (y[7] + k[9]))),\n",
    "            ((-1.0 * k[26] * y[19] * y[8] / (y[8] + k[27]))),\n",
    "            ((1.0 * k[26] * y[19] * y[8] / (y[8] + k[27]))),\n",
    "            ((-1.0 * k[10] * y[7] * y[10] / (y[10] + k[11])) + (1.0 * k[12] * y[28] * y[11] / (y[11] + k[13]))),\n",
    "            ((1.0 * k[10] * y[7] * y[10] / (y[10] + k[11])) + (-1.0 * k[12] * y[28] * y[11] / (y[11] + k[13]))),\n",
    "            ((-1.0 * k[14] * y[11] * y[12] / (y[12] + k[15])) + (1.0 * k[44] * y[31] * y[13] / (y[13] + k[45])) + (\n",
    "                1.0 * k[34] * y[23] * y[13] / (y[13] + k[35]))),\n",
    "            ((1.0 * k[14] * y[11] * y[12] / (y[12] + k[15])) + (-1.0 * k[44] * y[31] * y[13] / (y[13] + k[45])) + (\n",
    "                -1.0 * k[34] * y[23] * y[13] / (y[13] + k[35]))),\n",
    "            ((-1.0 * k[42] * y[27] * y[14] / (y[14] + k[43])) + (1.0 * k[46] * y[31] * y[15] / (y[15] + k[47]))),\n",
    "            ((1.0 * k[42] * y[27] * y[14] / (y[14] + k[43])) + (-1.0 * k[46] * y[31] * y[15] / (y[15] + k[47]))),\n",
    "            ((-1.0 * k[16] * y[13] * y[16] / (y[16] + k[17])) + (-1.0 * k[18] * y[15] * y[16] / (y[16] + k[19])) + (\n",
    "                1.0 * k[20] * y[30] * y[17] / (y[17] + k[21]))),\n",
    "            ((1.0 * k[16] * y[13] * y[16] / (y[16] + k[17])) + (1.0 * k[18] * y[15] * y[16] / (y[16] + k[19])) + (\n",
    "                -1.0 * k[20] * y[30] * y[17] / (y[17] + k[21]))),\n",
    "            ((-1.0 * k[22] * y[17] * y[18] / (y[18] + k[23])) + (1.0 * k[24] * y[30] * y[19] / (y[19] + k[25]))),\n",
    "            ((1.0 * k[22] * y[17] * y[18] / (y[18] + k[23])) + (-1.0 * k[24] * y[30] * y[19] / (y[19] + k[25]))),\n",
    "            ((-1.0 * k[28] * y[3] * y[20] / (y[20] + k[29])) + (-1.0 * k[30] * y[11] * y[20] / (y[20] + k[31]))),\n",
    "            ((1.0 * k[28] * y[3] * y[20] / (y[20] + k[29])) + (1.0 * k[30] * y[11] * y[20] / (y[20] + k[31]))),\n",
    "            ((-1.0 * k[32] * y[21] * y[22] / (y[22] + k[33]))),\n",
    "            ((1.0 * k[32] * y[21] * y[22] / (y[22] + k[33]))),\n",
    "            ((-1.0 * k[36] * y[5] * y[24] / (y[24] + k[37]))),\n",
    "            ((1.0 * k[36] * y[5] * y[24] / (y[24] + k[37]))),\n",
    "            ((-1.0 * k[38] * y[25] * y[26] / (y[26] + k[39])) + (1.0 * k[40] * y[29] * y[27] / (y[27] + k[41]))),\n",
    "            ((1.0 * k[38] * y[25] * y[26] / (y[26] + k[39])) + (-1.0 * k[40] * y[29] * y[27] / (y[27] + k[41]))),\n",
    "            0,\n",
    "            0,\n",
    "            0,\n",
    "            0], float)\n",
    "\n",
    "    \n",
    "    cpdef int evaluate(self, double t,\n",
    "                       np.ndarray[double, ndim=1] y,\n",
    "                       np.ndarray[double, ndim=1] yout,\n",
    "                       object userdata = None) except? -1:\n",
    "        #cdef np.ndarray[double, ndim=1] k = self.k  # avoid self.k gives quite some speedup!\n",
    "        cdef np.ndarray[double, ndim=1] k = userdata\n",
    "        # avoiding creation of temporary arrays gives quite some speedup!\n",
    "        yout[0] = ((-1.0 * k[0] * y[0] * y[2])) + (1.0 * k[1] * y[3])\n",
    "        yout[1] = ((-1.0 * k[2] * y[1] * y[4])) + (1.0 * k[3] * y[5])\n",
    "        yout[2] = ((-1.0 * k[0] * y[0] * y[2])) + (1.0 * k[1] * y[3])\n",
    "        yout[3] = ((1.0 * k[0] * y[0] * y[2]) + (-1.0 * k[1] * y[3]))\n",
    "        yout[4] = ((-1.0 * k[2] * y[1] * y[4]) + (1.0 * k[3] * y[5]))\n",
    "        yout[5] = ((1.0 * k[2] * y[1] * y[4]) + (-1.0 * k[3] * y[5]))\n",
    "        yout[6] = ((-1.0 * k[4] * y[3] * y[6] / (y[6] + k[5])) + (-1.0 * k[6] * y[5] * y[6] / (y[6] + k[7])) + (\n",
    "                1.0 * k[8] * y[9] * y[7] / (y[7] + k[9])))\n",
    "        yout[7] = ((1.0 * k[4] * y[3] * y[6] / (y[6] + k[5])) + (1.0 * k[6] * y[5] * y[6] / (y[6] + k[7])) + (\n",
    "                -1.0 * k[8] * y[9] * y[7] / (y[7] + k[9])))\n",
    "        yout[8] = ((-1.0 * k[26] * y[19] * y[8] / (y[8] + k[27])))\n",
    "        yout[9] = ((1.0 * k[26] * y[19] * y[8] / (y[8] + k[27])))\n",
    "        yout[10] = ((-1.0 * k[10] * y[7] * y[10] / (y[10] + k[11])) + (1.0 * k[12] * y[28] * y[11] / (y[11] + k[13])))\n",
    "        yout[11] = ((1.0 * k[10] * y[7] * y[10] / (y[10] + k[11])) + (-1.0 * k[12] * y[28] * y[11] / (y[11] + k[13])))\n",
    "        yout[12] = ((-1.0 * k[14] * y[11] * y[12] / (y[12] + k[15])) + (1.0 * k[44] * y[31] * y[13] / (y[13] + k[45])) + (\n",
    "                1.0 * k[34] * y[23] * y[13] / (y[13] + k[35])))\n",
    "        yout[13] = ((1.0 * k[14] * y[11] * y[12] / (y[12] + k[15])) + (-1.0 * k[44] * y[31] * y[13] / (y[13] + k[45])) + (\n",
    "                -1.0 * k[34] * y[23] * y[13] / (y[13] + k[35])))\n",
    "        yout[14] = ((-1.0 * k[42] * y[27] * y[14] / (y[14] + k[43])) + (1.0 * k[46] * y[31] * y[15] / (y[15] + k[47])))\n",
    "        yout[15] = ((1.0 * k[42] * y[27] * y[14] / (y[14] + k[43])) + (-1.0 * k[46] * y[31] * y[15] / (y[15] + k[47])))\n",
    "        yout[16] = ((-1.0 * k[16] * y[13] * y[16] / (y[16] + k[17])) + (-1.0 * k[18] * y[15] * y[16] / (y[16] + k[19])) + (\n",
    "                1.0 * k[20] * y[30] * y[17] / (y[17] + k[21])))\n",
    "        yout[17] = ((1.0 * k[16] * y[13] * y[16] / (y[16] + k[17])) + (1.0 * k[18] * y[15] * y[16] / (y[16] + k[19])) + (\n",
    "                -1.0 * k[20] * y[30] * y[17] / (y[17] + k[21])))\n",
    "        yout[18] = ((-1.0 * k[22] * y[17] * y[18] / (y[18] + k[23])) + (1.0 * k[24] * y[30] * y[19] / (y[19] + k[25])))\n",
    "        yout[19] = ((1.0 * k[22] * y[17] * y[18] / (y[18] + k[23])) + (-1.0 * k[24] * y[30] * y[19] / (y[19] + k[25])))\n",
    "        yout[20] = ((-1.0 * k[28] * y[3] * y[20] / (y[20] + k[29])) + (-1.0 * k[30] * y[11] * y[20] / (y[20] + k[31])))\n",
    "        yout[21] = ((1.0 * k[28] * y[3] * y[20] / (y[20] + k[29])) + (1.0 * k[30] * y[11] * y[20] / (y[20] + k[31])))\n",
    "        yout[22] = ((-1.0 * k[32] * y[21] * y[22] / (y[22] + k[33])))\n",
    "        yout[23] = ((1.0 * k[32] * y[21] * y[22] / (y[22] + k[33])))\n",
    "        yout[24] = ((-1.0 * k[36] * y[5] * y[24] / (y[24] + k[37])))\n",
    "        yout[25] = ((1.0 * k[36] * y[5] * y[24] / (y[24] + k[37])))\n",
    "        yout[26] = ((-1.0 * k[38] * y[25] * y[26] / (y[26] + k[39])) + (1.0 * k[40] * y[29] * y[27] / (y[27] + k[41])))\n",
    "        yout[27] = ((1.0 * k[38] * y[25] * y[26] / (y[26] + k[39])) + (-1.0 * k[40] * y[29] * y[27] / (y[27] + k[41])))\n",
    "        yout[28] = 0\n",
    "        yout[29] = 0\n",
    "        yout[30] = 0\n",
    "        yout[31] = 0\n",
    "        return 0\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model2 = egfngf_cython_model()\n",
    "# for the performance comparator, f_odes is the right hand side. \n",
    "# For cython odes, it must be CV_RhsFunction, so we make a circular link:\n",
    "model2.f_odes = model2\n",
    "models = [egfngf_model(), model2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Methods to use to solve the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class scipy_ode_int:\n",
    "    name = 'odeint'\n",
    "\n",
    "    def __call__(self, model, rtol):\n",
    "        def reordered_ode_userdata(t, y):\n",
    "            return model.f(y, t, model.userdata)\n",
    "        def reordered_ode(t, y):\n",
    "            return model.f(y, t)\n",
    "        if model.has_userdata:\n",
    "            result = odeint(reordered_ode_userdata, model.y0, model.ts, rtol=rtol)\n",
    "        else:\n",
    "            result = odeint(reordered_ode, model.y0, model.ts, rtol=rtol)\n",
    "        return result\n",
    "\n",
    "class scipy_ode_class:\n",
    "    def __init__(self, name):\n",
    "        self.name = name\n",
    "\n",
    "        space_pos = name.find(\" \")\n",
    "        if space_pos > -1:\n",
    "            self.solver = name[0:space_pos]\n",
    "            self.method = name[space_pos+1:]\n",
    "        else:\n",
    "            self.solver = name\n",
    "            self.method = None\n",
    "\n",
    "    def __call__(self, model, rtol):\n",
    "        solver = ode(model.f)\n",
    "        solver.set_integrator(self.solver, method=self.method, rtol=rtol,\n",
    "                              nsteps=10000)\n",
    "        solver.set_initial_value(model.y0, 0.0)\n",
    "        if model.has_userdata:\n",
    "            solver.set_f_params(model.userdata)\n",
    "\n",
    "        result = np.empty((len(model.ts), len(model.y0)))\n",
    "        for i, t in enumerate(model.ts):  # Drop t=0.0\n",
    "            if t == 0:\n",
    "                result[i, :] = model.y0\n",
    "                continue\n",
    "            result[i, :] = solver.integrate(t)\n",
    "\n",
    "        return result\n",
    "\n",
    "class scipy_odes_class(scipy_ode_class):\n",
    "\n",
    "    def __call__(self, model, rtol):\n",
    "        userdata = None\n",
    "        if model.has_userdata_odes:\n",
    "            userdata = model.userdata\n",
    "        solver = odes_ode(self.solver, model.f_odes, old_api=False,\n",
    "                          lmm_type=self.method, rtol=rtol,\n",
    "                          user_data = userdata)\n",
    "        solution = solver.solve(model.ts, model.y0)\n",
    "        for i, t in enumerate(model.ts):\n",
    "            try:\n",
    "                result[i, :] = solution.values.y[i]\n",
    "            except:\n",
    "                # no valid solution anymore\n",
    "                result[i, :] = 0\n",
    "\n",
    "        return result\n",
    "\n",
    "class scipy_solver_class:\n",
    "    def __init__(self, name):\n",
    "        self.name = name\n",
    "\n",
    "    def __call__(self, model, rtol):\n",
    "        def collected_ode_userdata(t, y):\n",
    "            return model.f(t, y, model.userdata)\n",
    "        def collected_ode(t, y):\n",
    "            return model.f(t, y)\n",
    "\n",
    "        if model.has_userdata:\n",
    "            sol = solve_ivp(collected_ode_userdata, [0.0, np.max(model.ts)], model.y0, method=self.name, rtol=rtol, t_eval=model.ts)\n",
    "        else:\n",
    "            sol = solve_ivp(collected_ode, [0.0, np.max(model.ts)], model.y0, method=self.name, rtol=rtol, t_eval=model.ts)\n",
    "\n",
    "        return sol.y.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "methods = [\n",
    "    scipy_ode_int(),\n",
    "    scipy_ode_class(\"vode bdf\"),\n",
    "    scipy_ode_class(\"vode adams\"),\n",
    "    scipy_ode_class(\"lsoda\"),\n",
    "    scipy_ode_class(\"dopri5\"),\n",
    "    scipy_ode_class(\"dop853\"),\n",
    "    ]\n",
    "if HAS_SOLVEIVP:\n",
    "    methods += [scipy_solver_class(\"RK45\"),\n",
    "                scipy_solver_class(\"RK23\"),\n",
    "                scipy_solver_class(\"Radau\"),\n",
    "                scipy_solver_class(\"BDF\"),\n",
    "                ]\n",
    "if HAS_ODES:\n",
    "    methods += [scipy_odes_class(\"cvode BDF\"),\n",
    "                scipy_odes_class(\"cvode ADAMS\"),\n",
    "               ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare the methods with the gold standard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gold standard for egfngf\n",
      "Gold standard for egfngf_cython\n",
      "method: odeint model: egfngf rtol: 1e-09 err: 7.494591409340501e-10 toc: 0.35076093673706055\n",
      "method: odeint model: egfngf rtol: 1e-08 err: 3.5497856151778252e-09 toc: 0.29200196266174316\n",
      "method: odeint model: egfngf rtol: 1e-07 err: 3.1049782798315086e-08 toc: 0.1904611587524414\n",
      "method: odeint model: egfngf rtol: 1e-06 err: 2.503034344408661e-07 toc: 0.17496275901794434\n",
      "method: odeint model: egfngf rtol: 1e-05 err: 2.5149287212601244e-06 toc: 0.1682443618774414\n",
      "method: odeint model: egfngf rtol: 0.0001 err: 8.5922166040109e-06 toc: 0.16126561164855957\n",
      "method: odeint model: egfngf rtol: 0.001 err: 0.00038126556983120586 toc: 0.07489609718322754\n",
      "method: odeint model: egfngf rtol: 0.01 err: 0.0012045689673627687 toc: 0.07285642623901367\n",
      "method: odeint model: egfngf rtol: 0.1 err: 0.008254680445357905 toc: 0.07691764831542969\n",
      "method: odeint model: egfngf_cython rtol: 1e-09 err: 7.494591409340501e-10 toc: 0.05883359909057617\n",
      "method: odeint model: egfngf_cython rtol: 1e-08 err: 3.5497856151778252e-09 toc: 0.04954171180725098\n",
      "method: odeint model: egfngf_cython rtol: 1e-07 err: 3.1049782798315086e-08 toc: 0.03227996826171875\n",
      "method: odeint model: egfngf_cython rtol: 1e-06 err: 2.503034344408661e-07 toc: 0.029372215270996094\n",
      "method: odeint model: egfngf_cython rtol: 1e-05 err: 2.5149287212601244e-06 toc: 0.02083730697631836\n",
      "method: odeint model: egfngf_cython rtol: 0.0001 err: 8.5922166040109e-06 toc: 0.054701805114746094\n",
      "method: odeint model: egfngf_cython rtol: 0.001 err: 0.00038126556983120586 toc: 0.015133380889892578\n",
      "method: odeint model: egfngf_cython rtol: 0.01 err: 0.0012045689673627687 toc: 0.02105879783630371\n",
      "method: odeint model: egfngf_cython rtol: 0.1 err: 0.008254680445357905 toc: 0.01078653335571289\n",
      "method: vode bdf model: egfngf rtol: 1e-09 err: 1.582705740778086e-08 toc: 1.3630039691925049\n",
      "method: vode bdf model: egfngf rtol: 1e-08 err: 7.414737328266103e-08 toc: 1.3709888458251953\n",
      "method: vode bdf model: egfngf rtol: 1e-07 err: 2.6406813733046877e-07 toc: 1.329686164855957\n",
      "method: vode bdf model: egfngf rtol: 1e-06 err: 2.173654429070666e-06 toc: 1.4558334350585938\n",
      "method: vode bdf model: egfngf rtol: 1e-05 err: 4.7244020594807805e-05 toc: 1.1136410236358643\n",
      "method: vode bdf model: egfngf rtol: 0.0001 err: 0.0003194977287663884 toc: 1.0986061096191406\n",
      "method: vode bdf model: egfngf rtol: 0.001 err: 0.0005875247020684765 toc: 1.0350258350372314\n",
      "method: vode bdf model: egfngf rtol: 0.01 err: 0.0006779951775676454 toc: 1.0145373344421387\n",
      "method: vode bdf model: egfngf rtol: 0.1 err: 0.0007534157881480254 toc: 1.005566120147705\n",
      "method: vode bdf model: egfngf_cython rtol: 1e-09 err: 1.582705740778086e-08 toc: 0.2337808609008789\n",
      "method: vode bdf model: egfngf_cython rtol: 1e-08 err: 7.414737328266103e-08 toc: 0.23300743103027344\n",
      "method: vode bdf model: egfngf_cython rtol: 1e-07 err: 2.6406813733046877e-07 toc: 0.2381274700164795\n",
      "method: vode bdf model: egfngf_cython rtol: 1e-06 err: 2.173654429070666e-06 toc: 0.2519502639770508\n",
      "method: vode bdf model: egfngf_cython rtol: 1e-05 err: 4.7244020594807805e-05 toc: 0.19704341888427734\n",
      "method: vode bdf model: egfngf_cython rtol: 0.0001 err: 0.0003194977287663884 toc: 0.19471335411071777\n",
      "method: vode bdf model: egfngf_cython rtol: 0.001 err: 0.0005875247020684765 toc: 0.181898832321167\n",
      "method: vode bdf model: egfngf_cython rtol: 0.01 err: 0.0006779951775676454 toc: 0.1848597526550293\n",
      "method: vode bdf model: egfngf_cython rtol: 0.1 err: 0.0007534157881480254 toc: 0.17755579948425293\n",
      "method: vode adams model: egfngf rtol: 1e-09 err: 1.1494973053534825e-09 toc: 0.8964440822601318\n",
      "method: vode adams model: egfngf rtol: 1e-08 err: 2.755623655199694e-08 toc: 0.9457309246063232\n",
      "method: vode adams model: egfngf rtol: 1e-07 err: 9.030623788324496e-08 toc: 3.319200277328491\n",
      "method: vode adams model: egfngf rtol: 1e-06 err: 2.1279329442101396e-06 toc: 1.3783111572265625\n",
      "method: vode adams model: egfngf rtol: 1e-05 err: 2.7317312334683568e-05 toc: 1.0065898895263672\n",
      "method: vode adams model: egfngf rtol: 0.0001 err: 0.00015762923472056477 toc: 1.0114195346832275\n",
      "method: vode adams model: egfngf rtol: 0.001 err: 0.00043566122939189274 toc: 1.0354232788085938\n",
      "method: vode adams model: egfngf rtol: 0.01 err: 0.0007342446711998006 toc: 1.0209317207336426\n",
      "method: vode adams model: egfngf rtol: 0.1 err: 0.0007754136760953892 toc: 1.012150526046753\n",
      "method: vode adams model: egfngf_cython rtol: 1e-09 err: 1.1494973053534825e-09 toc: 0.16318130493164062\n",
      "method: vode adams model: egfngf_cython rtol: 1e-08 err: 2.755623655199694e-08 toc: 0.1711866855621338\n",
      "method: vode adams model: egfngf_cython rtol: 1e-07 err: 9.030623788324496e-08 toc: 0.6225242614746094\n",
      "method: vode adams model: egfngf_cython rtol: 1e-06 err: 2.1279329442101396e-06 toc: 0.2435927391052246\n",
      "method: vode adams model: egfngf_cython rtol: 1e-05 err: 2.7317312334683568e-05 toc: 0.17838668823242188\n",
      "method: vode adams model: egfngf_cython rtol: 0.0001 err: 0.00015762923472056477 toc: 0.17899847030639648\n",
      "method: vode adams model: egfngf_cython rtol: 0.001 err: 0.00043566122939189274 toc: 0.18297147750854492\n",
      "method: vode adams model: egfngf_cython rtol: 0.01 err: 0.0007342446711998006 toc: 0.18081426620483398\n",
      "method: vode adams model: egfngf_cython rtol: 0.1 err: 0.0007754136760953892 toc: 0.18032503128051758\n",
      "method: lsoda model: egfngf rtol: 1e-09 err: 7.9299759818241e-10 toc: 0.30083489418029785\n",
      "method: lsoda model: egfngf rtol: 1e-08 err: 2.9678468126803637e-09 toc: 0.2960207462310791\n",
      "method: lsoda model: egfngf rtol: 1e-07 err: 4.500059920246713e-08 toc: 0.27411675453186035\n",
      "method: lsoda model: egfngf rtol: 1e-06 err: 1.0189701929145183e-07 toc: 0.15819025039672852\n",
      "method: lsoda model: egfngf rtol: 1e-05 err: 1.3192638509887426e-06 toc: 0.20678973197937012\n",
      "method: lsoda model: egfngf rtol: 0.0001 err: 1.3745593086108177e-05 toc: 0.129103422164917\n",
      "method: lsoda model: egfngf rtol: 0.001 err: 0.00025077333410454837 toc: 0.15668439865112305\n",
      "method: lsoda model: egfngf rtol: 0.01 err: 0.0017300367523133658 toc: 0.11679291725158691\n",
      "method: lsoda model: egfngf rtol: 0.1 err: 0.001518197812709744 toc: 0.26616454124450684\n",
      "method: lsoda model: egfngf_cython rtol: 1e-09 err: 7.9299759818241e-10 toc: 0.1106269359588623\n",
      "method: lsoda model: egfngf_cython rtol: 1e-08 err: 2.9678468126803637e-09 toc: 0.05040550231933594\n",
      "method: lsoda model: egfngf_cython rtol: 1e-07 err: 4.500059920246713e-08 toc: 0.08156490325927734\n",
      "method: lsoda model: egfngf_cython rtol: 1e-06 err: 1.0189701929145183e-07 toc: 0.02878880500793457\n",
      "method: lsoda model: egfngf_cython rtol: 1e-05 err: 1.3192638509887426e-06 toc: 0.03278160095214844\n",
      "method: lsoda model: egfngf_cython rtol: 0.0001 err: 1.3745593086108177e-05 toc: 0.02436351776123047\n",
      "method: lsoda model: egfngf_cython rtol: 0.001 err: 0.00025077333410454837 toc: 0.021378040313720703\n",
      "method: lsoda model: egfngf_cython rtol: 0.01 err: 0.0017300367523133658 toc: 0.014645576477050781\n",
      "method: lsoda model: egfngf_cython rtol: 0.1 err: 0.001518197812709744 toc: 0.013463258743286133\n",
      "method: dopri5 model: egfngf rtol: 1e-09 err: 3.487213689368218e-11 toc: 1.1242473125457764\n",
      "method: dopri5 model: egfngf rtol: 1e-08 err: 3.0333498822680366e-10 toc: 0.8492116928100586\n",
      "method: dopri5 model: egfngf rtol: 1e-07 err: 1.4577740997386476e-09 toc: 0.7640457153320312\n",
      "method: dopri5 model: egfngf rtol: 1e-06 err: 3.179589143352738e-08 toc: 0.7960407733917236\n",
      "method: dopri5 model: egfngf rtol: 1e-05 err: 1.7986466224707935e-07 toc: 0.84712815284729\n",
      "method: dopri5 model: egfngf rtol: 0.0001 err: 2.3582500277264748e-06 toc: 0.7310514450073242\n",
      "method: dopri5 model: egfngf rtol: 0.001 err: 2.2133255557008247e-05 toc: 0.7199337482452393\n",
      "method: dopri5 model: egfngf rtol: 0.01 err: 0.0002418223835189565 toc: 0.7273342609405518\n",
      "method: dopri5 model: egfngf rtol: 0.1 err: 0.003087030920236963 toc: 0.7233633995056152\n",
      "method: dopri5 model: egfngf_cython rtol: 1e-09 err: 3.487213689368218e-11 toc: 0.16988801956176758\n",
      "method: dopri5 model: egfngf_cython rtol: 1e-08 err: 3.0333498822680366e-10 toc: 0.13481640815734863\n",
      "method: dopri5 model: egfngf_cython rtol: 1e-07 err: 1.4577740997386476e-09 toc: 0.12070393562316895\n",
      "method: dopri5 model: egfngf_cython rtol: 1e-06 err: 3.179589143352738e-08 toc: 0.12108230590820312\n",
      "method: dopri5 model: egfngf_cython rtol: 1e-05 err: 1.7986466224707935e-07 toc: 0.12436628341674805\n",
      "method: dopri5 model: egfngf_cython rtol: 0.0001 err: 2.3582500277264748e-06 toc: 0.11689949035644531\n",
      "method: dopri5 model: egfngf_cython rtol: 0.001 err: 2.2133255557008247e-05 toc: 0.12184667587280273\n",
      "method: dopri5 model: egfngf_cython rtol: 0.01 err: 0.0002418223835189565 toc: 0.11848235130310059\n",
      "method: dopri5 model: egfngf_cython rtol: 0.1 err: 0.003087030920236963 toc: 0.11866450309753418\n",
      "method: dop853 model: egfngf rtol: 1e-09 err: 1.25751830637455e-11 toc: 1.3260033130645752\n",
      "method: dop853 model: egfngf rtol: 1e-08 err: 6.61048970111248e-11 toc: 1.14265775680542\n",
      "method: dop853 model: egfngf rtol: 1e-07 err: 8.888388742889219e-10 toc: 1.0359838008880615\n",
      "method: dop853 model: egfngf rtol: 1e-06 err: 4.66497442327333e-09 toc: 0.960273027420044\n",
      "method: dop853 model: egfngf rtol: 1e-05 err: 1.2721465764722477e-07 toc: 0.9472723007202148\n",
      "method: dop853 model: egfngf rtol: 0.0001 err: 1.1252778214596523e-06 toc: 0.9492876529693604\n",
      "method: dop853 model: egfngf rtol: 0.001 err: 2.291165946985075e-05 toc: 0.9411609172821045\n",
      "method: dop853 model: egfngf rtol: 0.01 err: 9.271624730080172e-05 toc: 0.8935632705688477\n",
      "method: dop853 model: egfngf rtol: 0.1 err: 0.0016197589501836288 toc: 0.8565003871917725\n",
      "method: dop853 model: egfngf_cython rtol: 1e-09 err: 1.25751830637455e-11 toc: 0.2147822380065918\n",
      "method: dop853 model: egfngf_cython rtol: 1e-08 err: 6.61048970111248e-11 toc: 0.18518447875976562\n",
      "method: dop853 model: egfngf_cython rtol: 1e-07 err: 8.888388742889219e-10 toc: 0.17031097412109375\n",
      "method: dop853 model: egfngf_cython rtol: 1e-06 err: 4.66497442327333e-09 toc: 0.14084482192993164\n",
      "method: dop853 model: egfngf_cython rtol: 1e-05 err: 1.2721465764722477e-07 toc: 0.1377122402191162\n",
      "method: dop853 model: egfngf_cython rtol: 0.0001 err: 1.1252778214596523e-06 toc: 0.13886094093322754\n",
      "method: dop853 model: egfngf_cython rtol: 0.001 err: 2.291165946985075e-05 toc: 0.13952231407165527\n",
      "method: dop853 model: egfngf_cython rtol: 0.01 err: 9.271624730080172e-05 toc: 0.1342453956604004\n",
      "method: dop853 model: egfngf_cython rtol: 0.1 err: 0.0016197589501836288 toc: 0.1288437843322754\n",
      "method: RK45 model: egfngf rtol: 1e-09 err: 3.6439984493578474e-11 toc: 1.4232659339904785\n",
      "method: RK45 model: egfngf rtol: 1e-08 err: 6.912577130909388e-10 toc: 1.2879347801208496\n",
      "method: RK45 model: egfngf rtol: 1e-07 err: 6.694102315426183e-09 toc: 1.196589469909668\n",
      "method: RK45 model: egfngf rtol: 1e-06 err: 8.223089846372508e-08 toc: 1.3194856643676758\n",
      "method: RK45 model: egfngf rtol: 1e-05 err: 2.2819689641437435e-07 toc: 1.329404354095459\n",
      "method: RK45 model: egfngf rtol: 0.0001 err: 3.917911855702793e-06 toc: 1.3202934265136719\n",
      "method: RK45 model: egfngf rtol: 0.001 err: 1.639140348158738e-05 toc: 1.3200581073760986\n",
      "method: RK45 model: egfngf rtol: 0.01 err: 0.00034920415170319453 toc: 1.3547723293304443\n",
      "method: RK45 model: egfngf rtol: 0.1 err: 0.0036178664214251087 toc: 1.3747754096984863\n",
      "method: RK45 model: egfngf_cython rtol: 1e-09 err: 3.6439984493578474e-11 toc: 0.5883312225341797\n",
      "method: RK45 model: egfngf_cython rtol: 1e-08 err: 6.912577130909388e-10 toc: 0.5303034782409668\n",
      "method: RK45 model: egfngf_cython rtol: 1e-07 err: 6.694102315426183e-09 toc: 0.48837947845458984\n",
      "method: RK45 model: egfngf_cython rtol: 1e-06 err: 8.223089846372508e-08 toc: 0.536247730255127\n",
      "method: RK45 model: egfngf_cython rtol: 1e-05 err: 2.2819689641437435e-07 toc: 0.5374157428741455\n",
      "method: RK45 model: egfngf_cython rtol: 0.0001 err: 3.917911855702793e-06 toc: 0.5388848781585693\n",
      "method: RK45 model: egfngf_cython rtol: 0.001 err: 1.639140348158738e-05 toc: 0.5385806560516357\n",
      "method: RK45 model: egfngf_cython rtol: 0.01 err: 0.00034920415170319453 toc: 0.5404636859893799\n",
      "method: RK45 model: egfngf_cython rtol: 0.1 err: 0.0036178664214251087 toc: 0.5524845123291016\n",
      "method: RK23 model: egfngf rtol: 1e-09 err: 6.681408073442678e-10 toc: 1.2001097202301025\n",
      "method: RK23 model: egfngf rtol: 1e-08 err: 4.8487383658842495e-09 toc: 1.1402056217193604\n",
      "method: RK23 model: egfngf rtol: 1e-07 err: 1.279591483277424e-08 toc: 1.0618393421173096\n",
      "method: RK23 model: egfngf rtol: 1e-06 err: 1.1492905757525781e-07 toc: 1.0311594009399414\n",
      "method: RK23 model: egfngf rtol: 1e-05 err: 1.6930524190926614e-06 toc: 1.027500867843628\n",
      "method: RK23 model: egfngf rtol: 0.0001 err: 1.2193154355724498e-05 toc: 1.0268914699554443\n",
      "method: RK23 model: egfngf rtol: 0.001 err: 0.0001278426051552712 toc: 1.0209898948669434\n",
      "method: RK23 model: egfngf rtol: 0.01 err: 0.002285091844560026 toc: 0.9941794872283936\n",
      "method: RK23 model: egfngf rtol: 0.1 err: 0.0527168081924734 toc: 0.8237001895904541\n",
      "method: RK23 model: egfngf_cython rtol: 1e-09 err: 6.681408073442678e-10 toc: 0.5616023540496826\n",
      "method: RK23 model: egfngf_cython rtol: 1e-08 err: 4.8487383658842495e-09 toc: 0.5162637233734131\n",
      "method: RK23 model: egfngf_cython rtol: 1e-07 err: 1.279591483277424e-08 toc: 0.49440717697143555\n",
      "method: RK23 model: egfngf_cython rtol: 1e-06 err: 1.1492905757525781e-07 toc: 0.4908134937286377\n",
      "method: RK23 model: egfngf_cython rtol: 1e-05 err: 1.6930524190926614e-06 toc: 0.48294854164123535\n",
      "method: RK23 model: egfngf_cython rtol: 0.0001 err: 1.2193154355724498e-05 toc: 0.4840507507324219\n",
      "method: RK23 model: egfngf_cython rtol: 0.001 err: 0.0001278426051552712 toc: 0.48421287536621094\n",
      "method: RK23 model: egfngf_cython rtol: 0.01 err: 0.002285091844560026 toc: 0.47213268280029297\n",
      "method: RK23 model: egfngf_cython rtol: 0.1 err: 0.0527168081924734 toc: 0.3859896659851074\n",
      "method: Radau model: egfngf rtol: 1e-09 err: 6.652087904512882e-11 toc: 1.3415403366088867\n",
      "method: Radau model: egfngf rtol: 1e-08 err: 5.170814498948554e-10 toc: 1.1088793277740479\n",
      "method: Radau model: egfngf rtol: 1e-07 err: 4.258043093917271e-09 toc: 0.8848395347595215\n",
      "method: Radau model: egfngf rtol: 1e-06 err: 3.060537681449205e-08 toc: 0.5140721797943115\n",
      "method: Radau model: egfngf rtol: 1e-05 err: 3.409584565088153e-07 toc: 0.41588926315307617\n",
      "method: Radau model: egfngf rtol: 0.0001 err: 4.311698737486343e-06 toc: 0.28522610664367676\n",
      "method: Radau model: egfngf rtol: 0.001 err: 2.4120030381309335e-05 toc: 0.13965725898742676\n",
      "method: Radau model: egfngf rtol: 0.01 err: 0.0004926898438409504 toc: 0.16023492813110352\n",
      "method: Radau model: egfngf rtol: 0.1 err: 0.0024953513627397478 toc: 0.0989832878112793\n",
      "method: Radau model: egfngf_cython rtol: 1e-09 err: 6.652087904512882e-11 toc: 0.7576034069061279\n",
      "method: Radau model: egfngf_cython rtol: 1e-08 err: 5.170814498948554e-10 toc: 0.5407040119171143\n",
      "method: Radau model: egfngf_cython rtol: 1e-07 err: 4.258043093917271e-09 toc: 0.41968345642089844\n",
      "method: Radau model: egfngf_cython rtol: 1e-06 err: 3.060537681449205e-08 toc: 0.28287482261657715\n",
      "method: Radau model: egfngf_cython rtol: 1e-05 err: 3.409584565088153e-07 toc: 0.1735553741455078\n",
      "method: Radau model: egfngf_cython rtol: 0.0001 err: 4.311698737486343e-06 toc: 0.1346416473388672\n",
      "method: Radau model: egfngf_cython rtol: 0.001 err: 2.4120030381309335e-05 toc: 0.08444905281066895\n",
      "method: Radau model: egfngf_cython rtol: 0.01 err: 0.0004926898438409504 toc: 0.05977010726928711\n",
      "method: Radau model: egfngf_cython rtol: 0.1 err: 0.0024953513627397478 toc: 0.05173158645629883\n",
      "method: BDF model: egfngf rtol: 1e-09 err: 3.1390972920538235e-09 toc: 0.5705592632293701\n",
      "method: BDF model: egfngf rtol: 1e-08 err: 1.2343679627520033e-08 toc: 0.5837991237640381\n",
      "method: BDF model: egfngf rtol: 1e-07 err: 8.442730720465382e-08 toc: 0.4266855716705322\n",
      "method: BDF model: egfngf rtol: 1e-06 err: 5.070871530430547e-07 toc: 0.327714204788208\n",
      "method: BDF model: egfngf rtol: 1e-05 err: 3.766178151369483e-06 toc: 0.32501935958862305\n",
      "method: BDF model: egfngf rtol: 0.0001 err: 3.5510810666213124e-05 toc: 0.3260772228240967\n",
      "method: BDF model: egfngf rtol: 0.001 err: 0.00017855276159510443 toc: 0.22342371940612793\n",
      "method: BDF model: egfngf rtol: 0.01 err: 0.0032314630305983884 toc: 0.13854002952575684\n",
      "method: BDF model: egfngf rtol: 0.1 err: 0.009260776487288725 toc: 0.17409443855285645\n",
      "method: BDF model: egfngf_cython rtol: 1e-09 err: 3.1390972920538235e-09 toc: 0.3882126808166504\n",
      "method: BDF model: egfngf_cython rtol: 1e-08 err: 1.2343679627520033e-08 toc: 0.36484789848327637\n",
      "method: BDF model: egfngf_cython rtol: 1e-07 err: 8.442730720465382e-08 toc: 0.2005314826965332\n",
      "method: BDF model: egfngf_cython rtol: 1e-06 err: 5.070871530430547e-07 toc: 0.18695449829101562\n",
      "method: BDF model: egfngf_cython rtol: 1e-05 err: 3.766178151369483e-06 toc: 0.1921083927154541\n",
      "method: BDF model: egfngf_cython rtol: 0.0001 err: 3.5510810666213124e-05 toc: 0.1263880729675293\n",
      "method: BDF model: egfngf_cython rtol: 0.001 err: 0.00017855276159510443 toc: 0.12924456596374512\n",
      "method: BDF model: egfngf_cython rtol: 0.01 err: 0.0032314630305983884 toc: 0.06199479103088379\n",
      "method: BDF model: egfngf_cython rtol: 0.1 err: 0.009260776487288725 toc: 0.04790949821472168\n",
      "method: cvode BDF model: egfngf rtol: 1e-09 err: 1.832806059004118e-09 toc: 0.11520576477050781\n",
      "method: cvode BDF model: egfngf rtol: 1e-08 err: 1.2058996459624419e-08 toc: 0.06452536582946777\n",
      "method: cvode BDF model: egfngf rtol: 1e-07 err: 1.2075101544420856e-07 toc: 0.05853128433227539\n",
      "method: cvode BDF model: egfngf rtol: 1e-06 err: 5.660151867535509e-07 toc: 0.04850339889526367\n",
      "method: cvode BDF model: egfngf rtol: 1e-05 err: 2.1248663150133023e-06 toc: 0.03807353973388672\n",
      "method: cvode BDF model: egfngf rtol: 0.0001 err: 8.0369563686448e-06 toc: 0.03027200698852539\n",
      "method: cvode BDF model: egfngf rtol: 0.001 err: 0.0006223608369100838 toc: 0.02919483184814453\n",
      "method: cvode BDF model: egfngf rtol: 0.01 err: 0.0019098949502052468 toc: 0.01840806007385254\n",
      "method: cvode BDF model: egfngf rtol: 0.1 err: 1532623.092336226 toc: 0.037409305572509766\n",
      "method: cvode BDF model: egfngf_cython rtol: 1e-09 err: 1.832806059004118e-09 toc: 0.01679062843322754\n",
      "method: cvode BDF model: egfngf_cython rtol: 1e-08 err: 1.2058996459624419e-08 toc: 0.013664960861206055\n",
      "method: cvode BDF model: egfngf_cython rtol: 1e-07 err: 1.2075101544420856e-07 toc: 0.015189647674560547\n",
      "method: cvode BDF model: egfngf_cython rtol: 1e-06 err: 5.660151867535509e-07 toc: 0.012308835983276367\n",
      "method: cvode BDF model: egfngf_cython rtol: 1e-05 err: 2.1248663150133023e-06 toc: 0.008781194686889648\n",
      "method: cvode BDF model: egfngf_cython rtol: 0.0001 err: 8.0369563686448e-06 toc: 0.007406711578369141\n",
      "method: cvode BDF model: egfngf_cython rtol: 0.001 err: 0.0006223608369100838 toc: 0.004948139190673828\n",
      "method: cvode BDF model: egfngf_cython rtol: 0.01 err: 0.0019098949502052468 toc: 0.004040956497192383\n",
      "method: cvode BDF model: egfngf_cython rtol: 0.1 err: 1532623.092336226 toc: 0.006505489349365234\n",
      "method: cvode ADAMS model: egfngf rtol: 1e-09 err: 1.9099610896470648e-09 toc: 0.24017071723937988\n",
      "method: cvode ADAMS model: egfngf rtol: 1e-08 err: 7.36613825817282e-09 toc: 0.21523308753967285\n",
      "method: cvode ADAMS model: egfngf rtol: 1e-07 err: 8.801648077981857e-08 toc: 0.13567042350769043\n",
      "method: cvode ADAMS model: egfngf rtol: 1e-06 err: 1.1504714326777807e-06 toc: 0.08283209800720215\n",
      "method: cvode ADAMS model: egfngf rtol: 1e-05 err: 4.671290605377484e-06 toc: 0.07387781143188477\n",
      "method: cvode ADAMS model: egfngf rtol: 0.0001 err: 1.0608413538041836e-05 toc: 0.058342933654785156\n",
      "method: cvode ADAMS model: egfngf rtol: 0.001 err: 0.00012178687288246389 toc: 0.052404165267944336\n",
      "method: cvode ADAMS model: egfngf rtol: 0.01 err: 0.0011556670401549067 toc: 0.04270744323730469\n",
      "method: cvode ADAMS model: egfngf rtol: 0.1 err: 0.005974432620540139 toc: 0.03546285629272461\n",
      "method: cvode ADAMS model: egfngf_cython rtol: 1e-09 err: 1.9099610896470648e-09 toc: 0.05116581916809082\n",
      "method: cvode ADAMS model: egfngf_cython rtol: 1e-08 err: 7.36613825817282e-09 toc: 0.036460161209106445\n",
      "method: cvode ADAMS model: egfngf_cython rtol: 1e-07 err: 8.801648077981857e-08 toc: 0.029713153839111328\n",
      "method: cvode ADAMS model: egfngf_cython rtol: 1e-06 err: 1.1504714326777807e-06 toc: 0.01849055290222168\n",
      "method: cvode ADAMS model: egfngf_cython rtol: 1e-05 err: 4.671290605377484e-06 toc: 0.015970945358276367\n",
      "method: cvode ADAMS model: egfngf_cython rtol: 0.0001 err: 1.0608413538041836e-05 toc: 0.014380693435668945\n",
      "method: cvode ADAMS model: egfngf_cython rtol: 0.001 err: 0.00012178687288246389 toc: 0.011634111404418945\n",
      "method: cvode ADAMS model: egfngf_cython rtol: 0.01 err: 0.0011556670401549067 toc: 0.00951528549194336\n",
      "method: cvode ADAMS model: egfngf_cython rtol: 0.1 err: 0.005974432620540139 toc: 0.007592439651489258\n"
     ]
    }
   ],
   "source": [
    "\n",
    "rtols = 10 ** np.arange(-9.0, 0.0)\n",
    "\n",
    "GoldStandard = namedtuple('GoldStandard', ['name', 'values', 'max'])\n",
    "\n",
    "gold_standards = []\n",
    "for model in models:\n",
    "    print('Gold standard for {}'.format(model.name))\n",
    "    result = methods[0](model, 1e-12)\n",
    "    gold_standards.append((model.name, GoldStandard(model.name, result, np.max(result))))\n",
    "\n",
    "gold_standards = OrderedDict(gold_standards)\n",
    "\n",
    "data = []\n",
    "for method in methods:\n",
    "    for model in models:\n",
    "        for rtol in rtols:\n",
    "            print('method: {} model: {} rtol: {}'.format(method.name, model.name, rtol), end='')\n",
    "\n",
    "            # Run\n",
    "            tic = time.time()\n",
    "            result = method(model, rtol)\n",
    "            toc = time.time() - tic\n",
    "\n",
    "            # Compare to gold standard\n",
    "            standard = gold_standards[model.name]\n",
    "            diff = result - standard.values\n",
    "            max_rel_diff = np.max(diff/standard.max)\n",
    "\n",
    "            # Append to table\n",
    "            record = (method.name, model.name, rtol, max_rel_diff, toc)\n",
    "            print(' err: {} toc: {}'.format(max_rel_diff, toc))\n",
    "            data.append(record)\n",
    "\n",
    "\n",
    "data = DataFrame(data, columns=['method', 'model', 'rtol', 'err', 'time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Icaqrq8NxnBFvIJlMZkTvCBEREREREREZpBBinFzXpaWlhQ0bNlS2WWvZsGEDixcvrmHN\nRERERERERKY2DccYRbFYpLOzszIvQ1dXF9u3byeZTNLY2MjJJ5/MbbfdRktLC62trTz11FOUSiWO\nOeaYGtdcREREREREZOpSCDGKbdu2cd1112GMwRjDfffdB8CqVat4//vfz1FHHUU2m+Whhx6iv7+f\nBQsWcMkll1BfX1/jmouIiIiIiIhMXQohRrF06VKuuuqqve5zwgkncMIJJ1SnQiIiIiIiIiLTgLFa\nC3JK6uvr0zKdNTKwLJLavzbU/rWl9q89vQa1pfavLbV/ban9a88YQzqdrnU1RCaVQogpSkt0Ts4x\ntDzbxJep5cFqW6bav7ZlTpf3ILX/IP0NTO4x9B40sWWq/Wtb5mS9Bw2EQSLTlVbHEBEREREREZGq\nUAghIiIiIiIiIlWhEEJEREREREREqkIhhIiIiIiIiIhUhUIIEREREREREakKhRAiIiIiIiIiUhVa\nonOK6uvr0xrNNaI1smtL7V9bav/a02tQW2r/2lL715bav/aMMaTT6VpXQ2RSKYSYoqy1WiN+Eo6h\nNeInvkytUV7bMtX+tS1zurwHqf0H6W9gco+h96CJLVPtX9syJ+s9aCAMEpmuNBxDRERERERERKpC\nIYSIiIiIiIiIVIVCCBERERERERGpCoUQIiIiIiIiIlIVCiFEREREREREpCoUQoiIiIiIiIhIVSiE\nEBEREREREZGqMNZaW+tKyEh9fX3opamNgbWZ1f61ofavLbV/7ek1qC21f22p/WtL7V97xhjS6XSt\nqyEyqRRCTFHWWtra2qpWXiqVIpPJVK28iSpzvMcYy/4tLS0AVW1/0GswQO0/ucdQ+09smdPlPUjt\nP0h/A5N7DL0HTWyZav/aljlZ70EDYZDIdKXhGCIiIiIiIiJSFQohRERERERERKQqFEKIiIiIiIiI\nSFUohBARERERERGRqlAIISIiIiIiIiJVoRBCRERERERERKpCIYSIiIiIiIiIVIVCCBERERERERGp\nCmOttbWuhIzU19eHXpraMMYAqP1rRO1fW2r/2tNrUFtq/9pS+9eW2r/2jDGk0+laV0NkUimEmKKs\ntbS1tVWtvFQqRSaTqVp5E1XmeI8xlv1bWloAqtr+oNdggNp/co+h9p/YMqfLe5Daf5D+Bib3GHoP\nmtgy1f61LXOy3oMGwiCR6UrDMURERERERESkKhRCiIiIiIiIiEhVKIQQERERERERkapQCCEiIiIi\nIiIiVaEQQkRERERERESqQiGEiIiIiIiIiFSFQggRERERERERqQqFECIiIiIiIiJSFQohRERERERE\nRKQqFEKIiIiIiIiISFUohBARERERERGRqjDWWlvrSshIfX196KWpDWMMgNq/RtT+taX2rz29BrWl\n9q8ttX9tqf1rzxhDOp2udTVEJpVCiCnKWktbW1vVykulUmQymaqVN1FljvcYY9m/paUFoKrtD3oN\nBqj9J/cYav+JLXO6vAep/Qfpb2Byj6H3oIktU+1f2zIn6z1oIAwSma40HENEREREREREqkIhhIiI\niIiIiIhUhUIIEREREREREakKhRAiIiIiIiIiUhUKIURERERERESkKhRCiIiIiIiIiEhVKIQQERER\nERERkapQCCEiIiIiIiIiVaEQQkRERERERESqQiGEiIiIiIiIiFSFQggRERERERERqQqFECIiIiIi\nIiJSFQohRERERERERKQqjLXW1roSMlJfXx96aWrDGAOg9q8RtX9tqf1rT69Bban9a0vtX1tq/9oz\nxpBOp2tdDZFJpRBiirLW0tbWVrXyUqkUmUymauVNVJnjPcZY9m9paQGoavuDXoMBav/JPYbaf2LL\nnC7vQWr/QfobmNxj6D1oYstU+9e2zMl6DxoIg0SmKw3HEBEREREREZGqUAghIiIiIiIiIlWhEEJE\nREREREREqkIhhIiIiIiIiIhURaTWFRARkRoJwPSFV20axdIiIiIiMukUQoiIzDRFiD8O8d+B0x1u\n8puheALY8y0mplm5RURERGRyKIQQEZlJcpbUtRDZMnyz2wXJe6G4dhexL8yqTd1EREREZNpT51sR\nkRnE+WVhRAAxVLCuROnmvupVSERERERmFIUQIiIzhMmCedbb537+EznIV6FCIiIiIjLjKIQQEZkh\nImvBlMawY8ESWT/p1RERERGRGUghhIjITLHvThAVYworRERERETGSSGEiMgMEcwZ+762bvLqISIi\nIiIzl0IIEZEZwl8CduEY3vYN1N8AyV+A0z759RIRERGRmUMhhIjIDBKcF8OavezgQvQzTeTPhOhr\n0HA11F8XzieBrVIlRURERGTaitS6AiIiUj32yAiFUyHx2Mj7gjpIfLIZ97gEhdZuCqdC9IVw39T/\ngt8C+VOhdDT67yEiIiIi+0UfI0VEZpKiJfYSlJZDcRVE3gQMeIuh9DaoX5wY3DcCpeOgdCxE1kH8\nMai/BYJ7oXAyFE/Q3BEiIiIiMj4KIUREZhDzQAnTB7lPhBNVlo4by4PAOzS8OO0QfxwSD0LiISge\nD4VTIZg96VUXERERkWnAWGs1yncK6uvrQy9NbRgTDphX+9eG2n8SdQa4/28W+84owfvio+4y5vbv\nDXAeK2EeLUEW7NtcgjUxOMQBs7dJJ2Rf9DdQW2r/2lL715bav/aMMaTT6VpXQ2RSKYSYoqy1tLW1\nVa28VCpFJpOpWnkTVeZ4jzGW/VtaWgCq2v6g12CA2n/yjlF3E0Q3G3o+b2H0DGL87V+C2PPhUA23\nA7xFUHg7lI4E3LEdAmZG+491/1r8Daj9B+k9aHKPof8BE1um2r+2ZU7We5BRmC/TnIZjiIjMAJF1\nEHsJ/I/GIF6YuANHw7khiqsh8np53oifQdAEhVOgsBpI7PMoIiIiIjJDKIQQEZnufEjeDt4SYHUE\n+icwhBjggLcyvLhby/NG3AOJB6DwZ2EgYZsmvlgRERERObAohBARmeZiT4OzEzKfgWQVunj6rZC9\nAMxZEH8SYs9A/AkoHRUO1fAXTXoVRERERGSKUgghIjKNmQwk7w+HS/it1S3bNkL+zyG/BmLPhr0j\n0t8DbynkTwNvBeBUt04iIiIiUlsKIUREprHEb8AC+ffUsBJxKJ4CxZMg+qdw3ojUDeDPDpf3LI5l\nmVARERERmRYUQoiITFPuFoj9HnLngq2vdW0AJxySUToK3M1hGJG8HRL3A6cVMMeB1apkIiIiItOa\nQggRkekogOQdEMwPV6+YavwlkL0InM5wvojYQyUa7ofiMWHviGBBrWsoIiIiIpNBIYSIyDQU/SNE\nNkPmU4Bb69rsWTAr7Knhvq+e0sP9xJ+A+LNQWh5OYukdCmi5dBEREZFpQyGEiMh0k4fkPVB8G3iH\n1LoyY1RnKLwjXMoz+lJ53ogfgT8/DCOKq9B/LBEREZFpQB/pRESmmcRDYPKQO7vWNdkPESgdA6VV\nENkQhhF1t0LiPiicBMUTwdbVupIiIiIisr8UQoiITCPOznCOhfwasE21rs1bYMJeHN4h4OwIn1Pi\nIUg8HK6mUTgVgjm1rqSIiIiIjJdCCBGR6cJC8k4IGqBwWq0rM3GCeZB7P+TfBbGnIf4UxJ4BbyXk\n3w7+UjRvhIiIiMgBQiGEiMg0EXkVoq9D/yVAtNa1mXg2BYUzofAOiP0xHKqR/iF4reG8EaWjmNKT\ncIqIiIiIQggRkemhFPaCKB0KpcNrXZlJFoXiaigeD5HXIf441N8MwT3hxJaFPwMSta6kiIiIiIxG\nIYSIyDQQfxycbui/lJkzNMGAtyK8OG2QeDycwDLxIBRWh4GEba51JUVERERkKIUQIiIHONMTTtpY\nOCWcP2EmClog+yEw74H4k+GcEfEnoXRkOFTDX1zrGoqIiIgIKIQQETngJe8GG4f8GbWuSe3ZBsif\nFa4OEns27CGS/m/wDirPG3E44NS6liIiIiIzl0IIEZED2Tqf2AuQ/SCaB2GoGBRPhuKJEH0lnMSy\n/kbwZ4c9RorH17qCIiIiIjOTQggRkQNVAO4tBbxFUDy21pWZopxwSEbpSHDfDMOI5B2QuB94ewFz\nfNh7QkRERESqQyGEiMgBKvYMmK0Bub9GQwzGwF8M2Y+A0wWxJyD+2xIND0LpaMi/PZxXQkREREQm\nl0IIEZEDkMlC4jcQnBjBX+zVujoHlKAZ8u+FyPvqKT3cT/wJaHg+XN60cCp4hzFzVhgRERERqTKF\nECIiB6DEb8AE4J8XAxRC7JekoXBaOEdE9KVwqEbqevDnhZNYFlcB0VpXUkRERGR6UQghInKAcdrC\noRj5syHa4ECm1jU6wLlQWhUOy3A3QuIxSP4SEvdC4aRwckubqnUlRURERKYHhRAiIgcSC3W3QzAH\nCifri/oJZcA/GPoPBqcjXN4z8VtIPBJO/Fk4FYJ5ta6kiIiIyIFNIYSIyAEk+gJENkLm44Bb69pM\nX8EcyP0F5N8V9jqJPwnx30FpZXneiEPQvBEiIiIi+8FYa22tKyEj9fX1oZemNowJzyzU/rWh9t+L\ngsX9ahZ7kEPwqeSkFKH234OSxTzr4TxUwrQF2EUOwZoo9rgIuBObRug1qC21f22p/WtL7V97xhjS\n6XStqyEyqRRCTFHWWtra2qpWXiqVIpOp7sDyiShzvMcYy/4tLeE6fdVsf9BrMEDtv2eJ+8LJE/s+\nB8Gs/TuG2v8tlmkhsjZ8HaJvgG005E+2FP8M7Bhyoan6HnTAtP9bPMZUbX/QazBA7T+5x1D7T2yZ\nk/UeNBAGiUxXGo4hInIAcHZB/FEovHMwgJAaMOAtDy/Odkg97ZL4jUfiQSiuDlfa0OsjIiIismcK\nIUREDgDJu8IVGvLvqHVNZECwAIKLE/SfkSH+FMSegtiTUDoyXOLTX1LrGoqIiIhMPQohRESmuMjr\nEH0F+j8CxGpdG9mdTUP+3ZB/J8SeC1fVSP8PeEvCMKJ0BODUupYiIiIiU4NCCBGRqcyD5B1QOgRK\nR9W6MrJXMSieBMUTIPIaJB6F+pvAbw5X1CgeD6RqXUkRERGR2lIIISIyhcWfBKcT+i9CS0IeKBzw\nDofM4eBuCSexTN4FyfvBnlrArAbbWOtKioiIiNSGQggRkSnK9ELiASieGM4/IAcefxFkLwTTHQZK\n8cdLNDwEpaPL80YsrHUNRURERKpLIYSIyBSVvBdsBPLvqnVN5K2yTZA/GyLn1lN6pJ/4ExD7QzjM\npvB28A5D80aIiIjIjKAQQkRkCnI3Q+x5yH4AbLLWtZEJkzQU3g6FkyH6cjhUI/Vj8OeW5404ttYV\nFBEREZlcCiFERKaaAJK3g7ewPJmhTD9uOCSj9LYwcIo/CslfQeI+KL2rj8iZdbWuoYiIiMikUAgh\nIjLFxJ6FyFbo+zTqoj/dGfAPguxB4OwKl/d07unHuytD8piwd0Qwv9aVFBEREZk4CiFERKYQk4PE\nvVA8Jjw5lZkjmA2590HjxfPwHs4S3NdH/PdQOqw8b8QytEKKiIiIHPAUQoiITCGJB8B4kPvzWtdE\nasWkHKLnpuhY1Uf0BUg8Bqn/Bb8F8qeGwzj031tEREQOVPoYIyIyRTjtEHsK8u8B21Dr2kjNRaB0\nHJSOhci6cBLL+lsguDec2LJ4AlhNHSEiIiIHGIUQIiJTgQ0nowyaoXBKrSsjU4oB79Dw4rSH80Yk\nHoTEQ+HEpYVTw6EcIiIiIgcChRAiIlNA9GWIrofMZeidWfYomA+58yH/bog/HfaciT0NpcOhcBr4\nS9j/eSPyHvEXO3C39wPgLU5TPHI2RN0Jq7+IiIiIPuqKiNRaEZJ3QWkleCtqXRk5ENg05N8F+XdC\n7PlwqEb6++AtDntGlI4ExpEdmN9tpemO1zGloLIt/mIHyQc3kz3nEEorZ038kxAREZEZSYu/iYjU\nWOK3YPog995a10QOONFwboi+z0HmUrBRqP8ZNHwjDCYojOEQL3cQ+eWrwwKIAU7ep/62tUQ29Ex8\n3UVERGRGUk8IEZEacrog/ttwCUaN65f95oC3Mry4W8vzRtwTrrZSOCGcyNI2jfI4a0k+smWvhzaB\nJfnIm/Qd3Dg5dRcREZEZRSGEiExPgcXpyGH8gKApgU1Ozbe7xF3hCgf50yf2uKa/ROzFDtzOPDbq\nUDq0CU+KG1vYAAAgAElEQVQnkTOC3wrZC8CcBfEnIf5MGEqUjirPG9E6uG9kYy9u9767S0S29ePs\nyBLM03IcInLgyPdvpf3Nu8l0vwpAqmkl8xefTaK+dR+PFJHJNDU/lYuI7C8/IPF0G/HnduD0FgGw\nrqG4chb50xYRzErUuIKDzGsesZeh/wIgPkEHtZbEI1tIPN2G8W1lc+J32/HnJAkuWQU6j5wRbIMl\nvyageLxH7DmP2B88Yi97+HN9/IM8bMrD3dI35uNF13dTTMembKAnMpWUir3s3Pobunc8Q+AXiNe1\nMG/RWTTMXoUx+zt7rIzH1nU/Y8sbNwKD/wsz3a+yfeOvWHToRaw45vLaVU5khtMnCRGZPgJL6pbX\nia4bPn7d+Jb4y7uIru8hc/Hh+FPh21wfnFuLeAdBadXEHTbxyBaST2wb9T63I4dz7XNw2UpoTE5c\noRPM93Ls2v4o+f5tuG6cpnknUt9wCHgBsVd24XbksI7BO7gRb0lDras7+QKLyXmYfPmS83Hy3uC2\nyn0+Tm74bRPY4cdKgpsB96UIOC7WMVgLYzknqnvwTeoefJMgGSFojuM3JwiaEgSzytebE9i6yD4P\nZq2P7+UIgqkTCE4ma30Cv4DjJnXyOUN073yWN/7w7wR+rrKtv3ctndsfpXH2sSw/9h9xIzPj979W\ndrx5D1ve+Mke7rVsWXsjqYYFNM5bU9V6iUhIIYSITBvxZ7aPCCCGcnIe9betpff/OrqKtRpd7Cmg\nPSD3N+z/koq7MZkSiafb9rFPkZ6f/4DNh7/BvMXnMHfRu3Gc6MRUYAK0bfgFW9f9DN/LVrZtWXsj\ny3LvZdmG1Tj5IZMnPr4Nb26S/r84dOoPE7AWSgH05HF3ZcOgYFiw4OHkfUzOoxisx2aLNPTlw7Ch\n4I9+SNdgkxFsIkKQjGCTEfzZCWwivB4kIuXrbmW/8OLivmmIPwrRly3gYaM7INqOcUqjlhXEXTIX\nrsDpLeJ25XG6CjideaKbenEyg4+xMacSSPizEgRN8cr1rNnB9k2/omPbwwR+DmMiNM8/mZal7yfV\nNP2Whcl0v8b2jb+is/0JrPVwI3XMWXgGC5b+BYm6llpXTyZJtm8jbzz/bwTB6MOcenY9z7oXv8lh\nx/5DlWs2c1gbsHXdzfvcb+OrP+Houe/EGM3TL1JtCiGmqHy2A9/P4zhxfXMyDl4pQ/fOZwn8HPHk\nAhpmH61/LjOFtcSfa9/nbm5HjsjGHryltZsfwWQg+QDYUyL4C70JO27sxZ3DhmDsSeuuVbzW+wAb\n//Q9drU9worj/3VKfCu3bf3PefP160dsb921iuWbjgNGrt4Q2ZkjfeMr9H3sSILmKjyHwGIK/mCA\nQIFoV1/YAyHvD+uZUNk2sG/5tdk98gkS7mBAkHShKYkzr55iUCyHB+7wYKEcKBDd//c2f0k4GWrs\nZQNuP5QWQKkFG+mA6E5wshgz2N5e6zz81jR+K4yIKYo+TnehEk6EP/PEXu7A6SlWMra0UyIdW0Jr\n/Fyy8U6y8S6yPVtYv/mrtBz7MeYuPnO/n89Us3PrA6x/6Wqwg23oe1naN99Bx7YHWXH8VaSbj6hh\nDWWytG345R4DiAFd7U+QzWwmldLvwP6y1gIWa4PyTx9sgLWW3s4/Uszv3Ocx8tl2ejtfpHH2BHZH\nFJExUQgxRd1z81nhFePgRupw3brwZ6QON5Iccn0s2+tw3cS0Phn3/TybX72Wjq0PDfvnH0/OZ9Hy\nS5izUN3tpjunqzCmCfYAkg9uxluYwoSfYQCLG4lSVyqF31gP2Y6lvJ8dHFY6ynUzZH8G9mfI9fJN\nYy3OLjAusNMh/SN/WFkD+5tRHjvadccYGoMgrEN+bIFG1E+y+o2LKEXy+E4J+8Z9JOevwkYdvFm9\nEHWJ5TIQdbBRFxt1IOJgo87I61EXnLcelJYKPWxZe9OI7SZwWbF17yenTs4j8dhWsuctG3uBXgC9\nBZyObNjboDzMYWSIsFuQkPdHdFxJQTisYSAwKIcFfvNgrwSbcAmSERLNabKUKj0SbCIyov3SLeG3\n5Pm2vfdqeavMQE9xPw2J1yBIhWGENw+wWCcHTj8Q4NfNDZf7HG3ukphLMK9u9N4oXoDd1c2mR/+D\nRDZJXaGZusIs5vWsIFloximvFB687OM3/h47J03QnBgc7tGcIGiMgXvg/P/K9m1kw0vXDAsghvK9\nLK8/91WOeef/TonwT/aftbZ84utjbYDv5+lo++2YHtu2/hck4i7Z/gyWgeME4U8CsAMn2OH2SlkE\ng/ti6YtFyeWzQ/YfckI+5LgDx4pGoxQL+SHHGXwOYNmxKQk2oL+/r1yf3esxUMbwegwtY9jjrD9Y\nD2x4e5TnYwwEgT/2444SSu+PUqFzQo4jIuOjEGKKOuld32Lnjq34XrZ8yQ1e97N4pV4Kue3Dtgd+\nfq/HdNzRQookkUgdiWQjgY3uOdAohyBOJInjTK1fmyAo8drvr6Kv66UR9xVy7ax74Rv4Xo75S86p\nQe2kanYf+74XTm+RiM2Uh0GY8Kfr4tqgMjTCDvRAMoRj3A3D9zfD97GOGXKfGRxiYQzWDB7HZMHt\nNXhLwF0YxfdKg/swtKyh5Q9utwy9zxCLxyiWimAMkbVdRLf1j6kNfKeEG0SJeXW4HQGR/k6Mb/D8\nXVDyqSt45WBl36xjyoFEOZyIOKMGGMPDCwcbcSvXe/qeZ3b3YnynFF6Mh++UmNO7jJhXv886xP7U\nQemwZvBteWjDyGBh2DwKXvjkdu8PY2NOOLRhSI+DoDFBkHSHbRsIFpKzG8kEhbBXwhh6rcVTKfxM\nZmwNO8lsZToNF/IrILoDEn8Kbwf14DWC1wRESDxliD8NwZxwdQ1/IXjln3udVDXi0J5/nPbUi2Fa\nM4SxDoliI/XlYGJ29DiazQqi67txuguVniPWQNBYHtbRHCeYVR7u0ZyA+NSb26R98x3lE7o980q9\ndGx7iPlLzq5SrcavcvI35CR78NvmwdsOCXKZvj3eP3gSHG7L9cbIZfuxBNjAh/LPgdu57hTW+vT0\ndO/leHu6HQw5yR3L/oO3jQHfL+2z/kPvfysnwh3b7qdj2/0T9nqFnLD3rHHKXzyVfxqDKV83jsvA\nP5bwPgdD+acxZHvjGOPgeX7lcRh38LiY8u1yWTgY42KcaPk+Z9hx91afge3GOMRicUqlcplD9g2v\nD6lr5T4zou7hPi79Peto2/DzMbWYG0nteycRmXBT62xSKhYsfgc2Mr5vwQYm+xo1uNj94g/elyt0\nku1bS6nYX9k2dCbh3TlOfA+9LvbWE2PkfdaO/aRxb3ZuuW/UAGKoTa9ey6wFb2fEp2CZNoKGGDbm\nYIr7/lCYO2MJxaPnDtuWSqXITPbJYQCp74PfDJlLIdWYIvsWy4ykUuTLx4i01BO9+bUhxQV4bh7P\nzVNyC3iRPCU3T2+ynfUtjw47zuyW00k1LieVrgcb0NPdjQkC8MDxLMYD44HjgfEsxjM4PhjPlK8b\nnIGfvoPxDU7Rwck5OP7AxcUJHFzfLV8PfxoM9TTQwsV7bz7j4RuPwPFwbISIHws/gALGh9StbwBg\nCShFi3iRAl60iBcphbfjRbz6Il60hBctEsR8im4BP1qkFC3hRzysY8sfkAeDnsoHdgDPQAbIhPdH\n2qL4/sAJp9nDY8uPNoZIJIrneZXbwx8XXt+2tg6DIZfLVe6jcpSBgMuMeNxACGYGE7NKHUY8DjDG\nEDvK4GQq6Vb5Hhue+eNiABtxMEc1keiaRbKjmcTWZhIvNpH0XSyWYmOG/Jwu8nN6yM/tIT+nBxvz\nKvVr33THqK+nNQG5eBe5eBewnq2RP7FkxSfKLzZEcoZYr0u0zyHWlyfa10d0g0Pijy6OP1BTS6rO\nUkz7lNIBpQafYjqglA4opnzsbmNfeuIJCoW9B/aF3jCa6unp3et+e7Jr2yNj2m/7xl/ilfoqJ83R\nqBvWbehJMAEMOUEfdrtyUrw/J92D33zbwB91/719Dpg8DsZxcIyLrZyEukNOQN3KtvHeNk50yO2B\nE+vBMqKxBJ7n7/t4DJzIhz+H3oaAdS98i7GEE7MXvJNlR15CLp8nPMl2h5zkDz2RHz0sGKhPKpWm\nvz9bPsa+g9B9/a9rKffEapvknli7m8j/wc3zTmTnlnvxSnv/G47GGmmcc8yElCki46MQYhoxxiUS\nTRGJjv9Ee+ibv7WWwM/vFlzsFmj4I+8r5Hbstq1/r98GGRPZS3iRHNELY7T7vITD9k137vP52aBI\nx9b7aZ516bjbRg4QMZfCUXNIPLdjr7sFyQjFw2dXqVLDRf8AkTeh71OAu+/9B4JFr5TBL2XwvH78\nUj+el8Ev9eN7/RhTJJ/twivftkd14JGj5Obx3eKY67Zr+6N07XgSx4lgjIO1gx+Eh50EDHwIjg35\nkFzZPvQD/tBvsYZuG34sg4OxLoWudeR7t+MG0WGXhuwC0rn5GOtirINjHRzrEhh/sNeEU8J3iuRW\npvDSFj8SduOtXGx4slo5qSqPJY5EopRKADHCU57B+wb2s4PjX0a9bRw37IUz8LjyPoMh68C28LoN\nHHzPG7g1OGyHgfHNEHhRsJZisVB5XGW4DkPqYO2wxzHkOVo78vmOfBww12LqKmOFhrWTLY8zsnGw\nWQsxCwuBhRYCQ13/Qhr6DqGh72DSOw9hzobDcIOwW0R/chu9qfX0pddRVz8HL92NF9l7Lx3fy7Lh\n5e+OfmeifJkbVi/upSpDO+ryzeH13lk0rW8mGgwOC8lH+8jGuipzUHTHO8kmwuueO7bhW5Mln9vO\n9o23VU6IXTeCtYPf5g7/29vTbQdjIhhn6Anz2E/S4/EkpZI3hv1HqUP55LsuWU++UGS0k/LRjpdK\npclm83sozxzwJ8Gd7U/S1f7EPvdbsPR9pJuXY95imQPv2TLIcaLMP+hcto4yxG+oRYd+YEpNzCwy\nkyiEmKKuv/8ykpG5NCRbaUi20phsJZ1cSNSd/PGjxpjyiX4S2P+TNWstNijtsRdGxPXJ9neNCDtK\nhS7y/cOHouxrkqex2Ln1QZJ1jXh+OM+G44ZDUZxIstJTw3H31q9Y9qaQbae741mCoESyvpXGOcdV\n/YNR/pSFxF7vGjZb/+5yaxa/pQn9xsPaAN/L4pUyBP1Z5t+1mMxh3WyP/QFvYz+uKZHNdpaDhf5y\nsJCpBApDV4jYneMmiURTxOJpjJPEjdQTT84j0rqYuldyRPMxon6CqJcg4ieI+nEifoLtTX/i9UUP\njjje8mO+xKz5J9XsBCDb8xwvPvnPI7Z7boEV2961z8f7jTF6Tz1mTEMiBkzEScd4jzGW/av9GsSe\nhuTtYHb78tYaKLwD8meN7Th9Pjgd4G6FyNaFzN22kAVb3o4pZ2HZRBu9qbX0ptfSk1pLX2odpehg\nW9Q1LONtp1w9rroPbc8AyNjycJyuPG55BY/6rjwNAyt65AbnTQmSEYJZCfzyCh7BrHC4x9yVB0F9\njO3bt4+rLgNeeOxvyGU27XO/2S3v5NCjrxz1uVRLLf4GYokURW9qDEmaDC1LP0DXjqf2OCcIQLr5\nqGm5IsxU0rrsQgrZNjq2PTTq/XMWruHgwy8t9yIRkWpTCDFFzW44mPbOtWzpfJaCN9idrC42h8a6\n1iHhxEIakotIJebhmDF8tVpFxhiMG8NxY0TjTSPuH88HlyDwCAZ6YwzpheE6Rf70u6+N6Rj5/i28\n/oery+M498QhEqsjGqnHmtiQnhfJco+MZKUnRjjHRnJwHzc5rKeGM80nAx1QzHfx+nP/H107nmFo\nF9RYYh5LVnyc2S2nVa0utiFO30ePoP7X64hsHf67FdRFyK1ZQnHV3D08epTj2aDSq2dkb4R+/FKm\nfF//4D5Dbg8d2nTY+o9j8wt5tukL5F/qwHGTRGMpHLeOSLQeN1JPLDmXSGQpbrSeSKQeN5oq/6wn\nEkkN3o7Ulb9pHP3vyBxaIPFUG/GXOirLO3bXb+GN+Q/TNmvk0KVofBZNc1ePp6kn3JyWU4gl5lDM\ndwzb3lO/je66rTRlW/f6+MKx88cVQMig4okQW12H91AWdyMYG873UDwhnANizFwI5oeX0nHlbQH0\nr32NXU/eTkPmUBoyyzhk818S8cPeCtn4dnrT6+hNrSV55EpMP9h9TwGyZ8Zg66L4dVH81vSIu1Nu\nnNyWznJIEa7i4XQWiG7sxekPw8sCf4JkhHRjfPgEmc1x/FkJbH10r79r8xa9h02v/nCfVZ23aIzp\njhxQ0s2Hc8hRn2XDS9eM2hu0Ln0wy4/5+xrUbGYxxmHZ0Vcyu+WdtG++k0x3OFQx1bSC+UveS9Pc\n1TPiM5rIVKUQYoo694SrKt+CFUp99OS20lu+9OS2sqP3T6xtvx8/CL9ickyEdGIBDXWLysHEYA+K\nRLRpTOMEpzLHieDE0kRiwz9U2jcfJO5BYQy/yUtWfpJlR1xIX2/n4JCRytwYucqwkvq6CF6pn57u\nncPuLxV6yhOADu5r7d5XIxgWVAy57oyyra6+Cc9zcSPJUe5Pht1VpxivlOHFx79Atu/NEfcV8ztY\n+8evEwQl5raeUbU6Bc0J+i47Eretn8i6Tnw/T6HJkj/IxQ+247WvCwOC3QIFY/Pk8z2VYQ5hoLDn\n+VEcN4EbqS8HCCki0XpiiTlEogdVbrvlACHRO5uFj60kc1ofR6y5JvwdcCKT9s2nbYyTO2spuTOX\n0Pfmc7zxp/+g5O65K3zrsgtrPuGs40Q4dNUXefX3/0zg54bd98LS2zjh9UtJeCNPKgFKy5oonNRS\njWpOX40O+X13OBk/B+oPW8GbPT+hbde14TZrqMstpDFzKOnMMhr7DuWQLRcQ2ZiEOyFoAm/h4ASY\nfivYiZrOJxnFb6nHb6kfdalRtyvPbOqwHf0UNu3A6SoQ39qB0zs4tMlGnXIwEa9MkDkQVtiGGHMW\nvZv2zXeSz27bYzUa5xxHw6yjJuhJyVQzt/VMUo2H0b75Trp2PE3gF0jUtTB38VnMaXmnel1WUdPc\n1TUP2UVkJIUQB4B4NM286ErmNawctt3agP5CB725beVwYgu9uW1s6niCTH5HOIEVEHXrhvWaaEgu\nrPSmiLpTb2bxsbJ+iZ5nr6MxgB37+E123ARzWs/EGIPjxnHcONF486j7jqcrdFAZbjJ0lZLckNu7\nBx3h/cXK/BmD99tgz0MIwucQHxw2MkrPi6G9MYaHGHXDgpCxrHASFPoIsjswbhwn3brHEKttw62j\nBhCDLJte/QGzF5y6Xx+6wvlJcuWeBZnhPQ52742wWw+F8GcWCCAH7PZyOk4ctxwURKIp4olGYvFZ\nuKklQ3of7N4bIbwdhghjHEdqof7u8MTKP6OBaDWHn0Yc0gev5qDEZ1j/0jUE/vBhTcZEWLT8kimz\ncky6+XCOPOk/2bbuZjrbn6iEfMHsOt48t8DijYcQf3kXphS+t/lNcQrHzaNwQsuELBMqk2f5sf/A\nuhe+EXZTN5Zs3VaydVtpm/cI9Y3LOeyYfyTRn8TdCu62cEhH/FFwynNIBo3lFTkGwolWsKNnUvsv\n5uLPr8ct/w/Itg1JPkoBTnd5iEd3HqczvB59tZN4T6Gykox1DUFTnJMar6Cj8Cw9zgay8S6y8S7y\nsW6ssTTNO5FDj/6/J7jyMtUkU4tZesRfsfSIv6p1VUREphyFEAcwYxxSiXmkEvNY2Dx8dl8/KNGX\naxvRg2Jb9x/Il3oq+9XFZtGQbGV2w1LqIvMqPSjSifk1/2Z0X0rbniHId9EA9EfDC5bKpPAVFg46\n9FIikVHWsH+LHCeKE2skGtt9ob/xq6tL0Nu9c8i8GTmGzqHhezmCSnAxGGoU87vw/S1hwFEONXY/\n2dydcWK4kSTRaD3GSQz20AgstncrNtOOEwQ4gBtvJt6ymkTrKUSidZWlXo0To/3Ne/f5vPxShvbN\nd9I45zi8UoZcb0Cmr2PU3gh+KcOrpkip2Ecx34tX6mdPs4wbJ1bpbTAQDkTjzSTqFw1uj6YqvRF2\nDxR2DxEmq1dC5FWIvgGZjwI1mv9qdss7aZxzPB1bHyDb9xpeqUh9wzLmLjqLWGJWbSq1B3Xpgzj0\nmP8Hr9hHIb8Tx4mRTC0CIHcE5N51EE53AVxDMCuhIRgHCDeS5LDj/ols3wZ2brmfYqGTZF0zDXNO\npmHW2wAIkuHwj9Kq8oMsOJ2DoYS7DeJPgFPuKBM0AItzJOaXlwttHbrs6ASLOgRz6wjmjvJ/xA9w\neorhEI/OPE53AacrT0vuWFq7D6/MtWGNxWuIQHcKf2d72ItiVnk+ikZ9My4iIjPH1D7LlP3mOlGa\n6pfQVL9kxH2FUobe/FZ6c9voyYa9J9q7X6G7/z688gSQxrjh8I7kQhqTrYM9KJKtJGOzqja8o+hl\n2dH7J/ygRFPdEhrrBseF+5nwq22DoaXfsisBqSLEAghMOKmaBdwAYq/dT9+bz5OP1+MRwUQSGDeO\niSQgEq9cN5EEffkFONEkXk8/xo1DJIEp74Mbn7Tn7jgRIqMMOdkfNvCH98AoXw+84eGG63jksj34\nfg4vu4NcxysEBASRsA3DduyC9t+El/20+bX/hdf+d9g2YyJhSFAOCiLReiKxRhoa5xGNpckXGDLc\nYTBQGLjtuLG32kyTrwTJO6G0HLyV+959MkWiKRYs/YuaTH63P/b4txBzCeZNfKAo1VGXPpiDDr8c\nGEPwZyCYHV5Kbytvs+B0l0OJrRBrh9hTkCjPLReky0M4Fu4WTEzmvyzXIZgVTmzpLdvtvsDi9IYB\nxeA8FAWim3tx/rgD4w2uf0JTglRTbMQQj6A5DtHqDMczvQXiL3Tg9BawMZfiiln4iye6y4mIiMx0\nCiFmoHg0xdzoCuamB2dmTqVS9PX1kS3uqvSa6M2GPSg273qaTP7XleEdETc5bN6JMJxYREOyldgE\n9TYoeVl+v/E61rU/iBcMrum+oPFtHH/wx5ibXhGGAmUGw5w8BFhK5XmGXAtRG37yNIkmMC5BIUNQ\n6Mf6eaxXwHoF8PNYL1+ZyXrvp2dmSGhRDjEqAUZ8SGix55CDIY8dGnIEiQjW2gkJOYzjEnH2vVzr\nwEmADXx67ricIDuyK4ktv/LWQPzoS3FbVuH7OfLZNjbuaTm93TTPO4mWQz5EJFJPQ9M8CgWzxxCh\nVqszTIb44+EJU/+lTO5JkMhMYSBoDi+loyCSSpLpy2B6ILJ1sMdE7BlIlKdBCeoH55cwyzzMLLBN\nVOdv0gmHZwRNcTh4tx5z1mIypbD3RFeeRH+Abe/D3ZYh9nIHpjjYCyxIRcNAYlZi2GSZfnMc4hPw\nUS6w1N27kdgfdlSGlgAkntmOt7CezAeWY9VbY5/cN/uIv9iB6S9ikxGKR8zGO7hRPbZERHajEEIq\njDHUx+dQH59DS9OqYff5QYm+/Pby/BNbyiHFNrZ3v0iu1FXZLxFtCgOJuoEeFAPDOxbgjnEMfcnP\ncc+L/8CuzNoR923veZF7XvgH3nPUvzJn4Z/B89cOWwbLwRDfrfe+k5xD+h3/gnHcvX7zZv0S1i8w\nb3YjQSnHjrY3w5DCy2P9Qjm0yIMf/rReAeuX7y/vExT7IburvH++HHKUHzfKLNlDdQ9c2WuoUb49\nhlBjMAQZ8tg99OQobXuGILtz1HoZDC6EX9VtfpLU4RcA0DBrFW3rb6WQ2/cydnNazyTdFHYFiCdS\nlKbx8mwDTDckHoLCqRDMq3VtRKYxE4YKpSYoHVneZsH0DiwXWg4mngXn4TyNQFA3ZOLL8jwTQTPV\nDQuNwaZjeOkYHNRALJWif+D/k7WYrDfYe6KzgNuVx92RJfpaJ05+8P9JUBcZ3nti1sBqHglscmwf\n8+ru3kD8j6P/D4hs6yd90yv0feyoMR9vpjE5j/pfvEF0U++w7fEXOvDm15H58GHYBoU4IiID9N9E\nxsR1ojTVLaapbjFw4rD7il7/kMkxw94TnZl1bNj5KF55lnuDQyoxf1gwMX/WMmJmNnWxWcOWSXrx\nzVtGDSAG+EGBx17/Nuev/iHJJSeT2/T4XuseX35OZTnDvTFuFONGiabCM8ZIfmK7+9vAC8OIcnhh\ny+HFQEgRj0Cuv3dYqBGGIIVyz418GHLkOofcP3g8gr2v1FExJOToiyYJnBhBbteYHup3rSUo9OLE\nGzDGMH/JOeFQi72IJebSPO+EsdVtjIJCL6W257BeHjc1n8j8VVNuqa3kPWDjkF9T65qIzEAGbCN4\njeAdMbg55deRfz2Luy0MJ2LPg/NIeF+QDMOI0vJezNIoTh0Es6hNLyZjsPVR/Poo/qKRwyFMzhsy\nxKNQCSui67txsoP/C4KEWwkk/GGreSSw9eFHQKcjt8cAYoDbVSD2/A4Kpyyc2Oc5HVhL6uevEdky\nergeac+S/umr9H78KIhNvVWuRERqQSHEFPWtP74AhQJ1rkt9JFL+6VLvRqiLuNS7LnWuS8Sp/YlX\nLFLPnPRy5qSXD9turSVX7CwHE4M9KLZ0/Z6+tjuw68JvciJOnHR5SEc6sYBX2+7cZ5l9+e1s7XqW\n5ad9ge3dW/F7No66X3TxqSRWnv+Wn+NEME4EE0tBbPQhEvWpFPYtjNWvhBxDem0M9tQYenvgep6o\nYylm+ygV+vawEOVIvff9HU5qAU7dXBrrZrMgdhA9/ZvwHPCccNjGAMeJs+zov5uw5UWtlyf73LUU\nNj0E/uCSeU79fJJvu4T40tMnpJy3yt3w/7P33nGSXOW99/dU6jx5ZidszrustNJKAiUMCEkgQEhk\njAW2ua8BwYsx9ouxuYC4BmO/NuE6ASYYCWx8MRhLgAAJkDBodxVXcaXNeXZmJ890rnTuH9Whuqdn\npnd30u7W9/PpT1WfVKeqq7vr/M5zngeMZyD9ZiC80L0JCAgo0ahgbwJ7ExTd94pk2fml1gvOw1nk\nT9M0AG64bClRsphoARb4r1dGNJxIHKc7PjnUaN5BLUTwUEbzBT8UOULHJ1CS5dLSUKA1Sjw3fWSm\nInumlXMAACAASURBVKGnAhGiFvqBsSkFiCLqcA7juSHMbUvmqVcBAQEBi5tAhJhD9u7dy/333w/A\nNddcw7Zt2+qu++jAIBP5HGnbIefWjg4AEFIUYlVCRVTVClsvPVYrvbAfUpQ5c7QohCAaaiUaaqWr\n6eKKPNe1cdQk/UP7Kywo+sefwXIydbU/MPE8G5e/nIbr/39y+35M/uB9uJkBANTmNYTXvRZj1Str\nzpAfy2S4/9QAx7NZNCG4pKmJ69rbiGnn7leiJHIwvR8IP8XlKdk9d5N96hszV9Ai6D1XIrNDOBPH\nsfp2kciNkPBNFdpCYisCwk1E2y/GGDyMmUmhRNtRou3IWOwMzg6kY5L81R3YQ89PynPTp0g//Hmk\nnSW89qYzan/WcCD6I7CXgXXJzMXPBezxo9iDz4N00ZrXoLUtsJfNgNPGzQzj5sdRQo0o0daF7s6i\nQibA3uC98kBD1xLkhMPQkwNlB5jPgvIbr7wbcnGWWDjLdJwexRMmWllwYaJEyAs16iyp8VtrOZ4w\nMeYJFKGkg3j2VF3NqmN5Gv7xSS8criI8CxEhUDSVhHQr0qQQ3vXwbWUhX9V1Yq7jWasowisjKNW3\n4qdACCLZTCHNa0MKUdFezTR/m6VywhPHha/fNdJE1ELN53x98R/Dl+avrwiMJ+q7fqGnB2uKENpB\n0J8CJQUyBuZWsNcypQWOyNkYzwyh9SY9R62rWhAbG4OlMgEBAecUwS/WHOG6Lvfffz+/93u/RygU\n4p//+Z/ZtGkTkUikrvr/fsMrS475HClJ2zYZxyFtO2Qcu7B1SPv3bZu04zBmWZzMZSvSp5IxFCCm\nacR1nYgiSkJFTWFjCpFDPQMRQ1E0GmLL0GVzRfpo+ij37PpAXW0cHHgQ+bxFXO+maekWmtbfhO4C\nQkExag90HSn58qHD3H9qoCJ919g43zl2nD9et5bXFxwjXkiEVl1H9tlvV1gX1CK8/nVEL35XRVos\nYjAxcBQ73Ud28Fn07DBh24J8Cnf4IJnjD1e0O66GENE2lGg7aswTJpRYR0mkcNtbUbTJS2HyB++r\nKUD4yTz5dYxl16KEFs6bu/EYKKcgdTuLZ1ByhjgTJ0g/9k/Yg89VpKvNq4ledjt6IEYseqy+XWRf\n+D72wLOlNK19C+FNb8LovnwBe7a4EQ0q9nqw13vvpZ0j//RPYfdBjLF2jPG1hE6tI7zdW74nDV9E\njqLFRBuL7zdA96LLFCPM6PE4ciKLsWdkxqpSgLmlzfNXIQFXggRd1bBN0wtFKr00pATXX66Q7kqw\nXYTleO0U2hCF8khwh01wJbppldK89orlXKRlgmOBFAgUUDSEVCqPewbMVYRXAGUki0iZyJgOQiBS\noH41Q/xIZTljl3cfpd85OeSs8cwg0fuOICzfU92eERp/LshcvwJrbRNaXxpsUEZjaAdDiLwXNcZa\nVxA36p+nCAgICJgzAhFijujt7aWjo4NEwhsMrVu3joMHD7Jly5bTbksVggZdp0Gvz7FjNVJK8q5b\nFjCqxIy042ApCmPZbCm917QqhI38NNYYYUWpaWkRnSRm+JeVqLRrOjhOhTVGItKFocUx63BcGDVa\nOTG0i7H0PaXIHVGjjebYCpqiK2iOea/GyDK0QiSNb+w/MEmAKJJzXf5m335Wd3WypaXldC/zOY0S\naiCy5bfJPn3X1GViSwhvuGVSulAN1EQXaqKLUOdkax8pJTI/gZsZxE0PoDkTZEdP4KYHsMcO4/Y+\nhsyX3HIyDmjRVmSkDbUgViixdrIv/NfMJ+KY5A//ksjGW+s679lGZCD8czAvA2fpgnRh1nCSfUz8\n8s+Q+fHJeaOHSD74P0m84i8DIWIRkzt4H5nH/gmqFlvZg8+RGtxN9LL3EV73moXp3DmEtHNMPPhx\nnOG9YEDG52hWseNElKtJLHkP2kAI/QUIF9wUSQOcrkpxwm0HznJ1mnawIHYOAxpYG8G83JtFPxOs\ntU11iRDWumZyL182KV2Nx8mexlLCmUKzFiMkjdSIkGSdepbU9s8izcn19Z4riV/9EYRrENoBxiMS\ndcy7990GSX6bxHwJoHuChieMlIWSaCRKJpWuElnKZYuCSYWoIiWRB46jDWVnPG8l79L0908iQypO\nSxhlKIJIRUANew5JZIiiaqX1QvybkHw/UHj00/eNEP3xoZoGEsKWxH52BCkoCTBS4oWGkRr0S4x9\nBthtyB4F/Rpf2NuAgICABSAQIeaIZDJZEiAAEokEExMT09SYO4QQhFWVsKrSSm1nizM9FNiuW7C8\ncMjYky0wilYaaccmYzuMWia9OackcmSmscZQhSgIFypRTcUx/gDTPY5OHp0cmvS23iuPRo6EFubS\nDX9BZ1MzdiZJKnuC0cxRxtJHGc0c5ejQdnb3/sA7fxQSkU7i4WXsHg/RTRsTooM0LcgqXwW2lNy1\ndx9/e9WVZ3Stz2Uim94MQiO3+/8grXRFntb+ImJX/glKqHGK2lMjhECEG1HCjdCylng8jlJ1r0k7\nj5sZws0MktBNrGQ/46cO4aYHsUcPeZE76nS8md//Y9yJ46Bo3uyYooGiIhQdhAqqhhBaIb+Qrqgo\n0TimaXt1RLGuWtVOuT0U3fN1UUwXgvDPvQfA3I2nfZkWHZmnv1lTgCjhmGSe+DKNr/q7+etUQN04\nyT4yj3+JagGijCSz65/Rl1yM2nCOK2ZzTObpuzwBogauliLN/ViRHPHf/ggAIlvwMVH0M7EPQju9\n8lIvCxNOD9jdheg59QgTJsT+HfSqrmhHIfwApN/u+bo4XczNrUQePI6Snt43RP7yhfVn4IwfJ/mb\nvwA7VzPf6n2YzPYvseT5P0I7BoW1IQAoExD5Fej7IfU/AF808dI3JB7FDU894TIV5kgO7RfHZiyX\nv7gda30TynAOfX8WkcuCMYoQnn8sKYUnRLgRcCOow2FCD0XIXx2GkEbkv0/M6CPVbwEiBKBW/pej\nD8Fggti/ryd3SiN3/emd63wg7Rz5Iw+SP/gznIkTCEVH77qM0PrXobedwQ0eEBCwKAlEiBocPXqU\n7du309fXRzKZ5O1vfzsbN1bO9j366KPs2LGDVCrFkiVLeM1rXkNPT88C9Xju0RSFBkU5K2uMnOtW\nCBaurjOcSnkihc8qI2lqHLXGSLoJLEJYIoxNCEf4BBQJ9z1VNi/2rDGaiKqtxLQriCU0Io0SVWZR\n3AnG7BEm0n1osp/l9KK7ORQsTKJkRQNp0coEHSRFOzv6YDSfr3EW5z+RjbcSXvtqzGO/wUn2IVQD\nveclaM2r5vS4QguhNvSgNvTQXJgFc3yzYK6VYew/31ZXW9LOYo8dBtcB10JKBxwbKW1PyHAdpGt5\n+b6wqelp2qwHI7eWxn2fY2TpvzLx8/vLYoaqFcQKvSB6aCXhIqOHcKSoFDMmiR3ldhBVooqiIirE\nk8nH8R9PKBqO5iCtvCfIFIWWKqzUIFbvozOeszN6CHt4L1rrhrO8egGzTf7ATyrCF9dEuuQO/JTY\ntj+Yn06dg0grQ/7wL2csZ57YgZsdRYk0IyNgr/FeJXKg+YWJA2A84g0apeYJE8qKPEZHQaBYwiRh\nIvqDyQJEEWF5AkXqvZ64cVpoCqk3rSPx3b2IfO1Q0tmXLcVeefoi9GyS3fNfUwoQReKPrUObJtiT\n1guRH0P2zbPXL/PidsIP9VaETa1GqoLcNd24zZ6nYm0PkAWQSGGBkgWR87ZKFrQhhGIS3QnRnV4Y\nVn/Uk7NBqElkZC/hBzZjrRE4c/v3flq4+QmSv/oEzuihUpp0TMxjv8Y89msiF7+LyOa3LGAPAwIC\nZotAhKiBaZp0dnaybds2vvvd707Kf+6557jvvvu4+eab6enp4eGHH+bb3/42H/zgB4kVnO5VWz4k\nk8nzWqSYCSEEEVUlopafquLxOKlI7dABWbOLhw98iWPDD5eWWrgoRCNr2bjsHcRjG8k4Do6mM5xO\n1bTKGLUdMrZG2mkkY8dIud1Tzjap0sQgi0GWiHuE23/2VRo1g6ZQjLZwI+3RdlpCDTToGnFNI6F5\nW2WOnHouBJbr8vDICM+MT+DK1axo38Ir2tuJ6Av/M6HoUdTm1RUPJlMR2fIOwuteW1e7UroFscIm\nFg2TSo4VRApPsChuy/sFAUM6FfnSsWm59wrsxjTmyxoIc4uXJ4t1PUGkum0hADuH6zpVx/MJJW5Z\nQPG3U5y/O13xZGxSiqgQKSY0w7sucuoHaj+pnZ9HiRQcHZa+D77vRa00X7rAc/SWUTUcx6ksW6oi\nfGnV37la6ZVpoma6IKdp2LZ9xn0HQV7XsSzbd/iqOv50ITA1Hcu2qspU1umLxQBBOpOe8thi2n4K\n8ocfoB7yRx5EaCFs3cC0zMnHq8kMZWZswitgGwamWcsPzcx9KC7hc6Zsozbl8lMfY+BoAiEgm0zh\nTPSCPbOpPa5NetdX0VrWFZr27jHh20cAHcJ7bRMIS0UbbkQfaUIfasDY3UTkoSgCgVQd7JYUVmsS\nu20CO5oidyxNvkmW26bQtix/P5zfCMxrfPeDqN6v/A5kIxHMXA5UQf5mG2PPCNqRJML2fl+czhjm\nxlacpSMwMFo+D197+UwUO5ud1Hblfrmeacaws5nCGYiK64UQ5CM2AoGTKoYNFUjXxjz639N/Bk6Y\n2Ng1SGxArfyO+DCegdxNdSxfkYDtCTxYldvSvgnYGvlNLyL8+CA4AqSC59GysEXFXhIn8p9Gqa5S\niogqCut2DKCx6vAOUs3htmZRrDTSSYHIg7BKH6uUiqdiSR2kWriOEs9Bh+NthV2ytigdVU0h1XFC\nO5vILCIRIr3z89P+z2ef+RZqwzKMpReetWpAwPnGwo8uFiHr1q1j3Tov3KSUk01Zd+7cyeWXX84l\nl3iu71/3utexb98+nnzySa699loAenp6GBwcJJlMYhgGBw4c4GUve9n8ncQ5TsRo4hWbP0Y6P0Tf\n2NO4rkVjdDlLGjdXlPOWkUSnaKWS7x0/wXdO9NbMc9AxC2KHIzRiqs5x02FfzsUcz+KIfqC/oo4A\nYqpKQtdJFISJhK6V96dIC6mLL074c+MTfGH/AUatSnPcfzt2nHcsX8Yt3QvvrDO09jVkHvvH6Qtp\nEUIrX1F3m0IooCqg6iihOIpVn6ikHgP9OW/iym0EGQHjFKTeDZG1b6j7+DMtg5oKKaU3y+3axKIh\nUslxn0hRKaAUxZOiVUjY0MhmUpPEjqJoEo9GMEePML7nZzWP7QLDaiMjWgMuChERpdtoIq6pxc75\ne1qVJv2pFelC0wB7Ulmqy5Zm96W/oarjFTeyfL1q9MnRVFzbmZQ+6fjT9MlSVRzHnqZsZZ9cReC6\n7rT9T496f82maZIqCKuudFGFKIVnLo0tJ/1HeevVq5dUTYmVwTz6ayxF8e6fmZjR4d9MBcr5llA8\nweu02i9/rgCmELi1rsEUmEIpXP+psZTCgN51Z3TWW1Hv5GPY/U+We+m/F6Sk8h6r2o8DMYnoDmHk\nVhHKriWUXUPoyBoiezYgUJHCwgwfJR85QD5yEDNyEEdNeYPtohCRA35Z2JfCNxAvpxXfCwRWVVoe\nAbFCLSkgI2BX4SUpCwfFdqXChK89ivV85bz+lfuQLPZH+vJ9dftL8oqoKJfgunLfpf+8lFLd8Y7v\nl9OlJwQIqaO4IYQbRsgQihtG+ycDQQghDYQbQnENpGsQd3WEYyBcHcWtvXy1FlKEcNVOJAV1Ahup\n2EjDwo1YSCWLdF1k2EXGXNSci8hJELJwP3v3g5AqSB0hNYRUPf8R7SrKRBwtZSCkVhAbXIRiI0Qe\nlAyoaYRwCn4gop4vCDcEMgxuyJvMUdOgDyCUgjWJNoi+v6nuc5xrzJHDWP27ZiyX23t3IEIEBJwH\nBCLEaeI4Dn19fbz0pS8tpQkhWL16NSdOnCilKYrCjTfeyJ133omUkmuvvXZSZIxPf/rTUx7n4x//\neMk503zh92GxeI7ZxVqm955Ub79vjSf49xO9tR9PhcAhTJYwm5qa+OZ1LwfAdR1G0yc4OXqAY6OH\n6R3rpS81wFBmApMQphMF2Yoi28m4TYxZUfLSIGVD0rJwaohYIUWhwTBoMAwaK16jNIR0Gg2DBt2g\nMVTOS+g6mjI3btZ7bYfPPPo4eWfyAMSUkjuPHqOtqYk3r1k9ZRune+/UU776/pdLbuPY0FOkDj9U\nu4JQWHrjx2lcvqZ2/iz0S446mP80inuwxtrpNoW2qzoQWn1CRr3HrIfG1tn9rbBz40wceBBpVy5L\nOqov4R863saoNtmH/Cu6u/nUFZctSpHtXGT3yCh/smMnYzVm+ZfGYvzdtVfTM02422M/+gjJAw/O\neJzEqqtZfssXz6qv5zO54UMc/NZb6yq78o3/SGzppWd9zP84eJD/s+8AfZkM0EfYOcVnn9nA1t4E\nRnY1RnY1Cf+g/wLAG6j7BEghfenFl/AG8aUyxT3PGkAKBylspLChQULMQao2aDZSSeOqY0jV8l6K\nhVRM7yXyuKqFFHmkYuIqucJ+HpccUmSRwiqIwC5S2kjHW+4nXQeki3Rtb2mg66XJ5Q5Qn7UZAM2F\n11S4Cka+m0huBeF8D7rVgm43oMiykGLmexjq+D65+POFSCKqJ8L82EAoSmE5n8qB4nI+oSIUpbAt\n+khSEUIp+UsSQvX2heJbKlhMK+wX2q1IK7VVTk8e+k1dl8Ie3E1bg44ea6v/+k3DbPwHz8UzUEDA\n+U4gQpwmmUwG13WJxytjHMXjcYaHKxcibtiwgQ0bzmyt9L7RDKsbI2jKhfOQMdcsT8S5prOTh/r7\npy33jnVrS/uKotKaWEFrYgUXLS+XsR2TkeRRBscPFF77GJw4yHj6ZKGEoCm2jIaG9URjazAiy9BC\n3ThqI0nLYcI0GS+8JkyLvkym9D5j1173mdALAoVh0GDoVQKGUcprNDwBo0HXiWpayWx5Kr72wgs1\nBQg/X39hD69fuQJjAQeYQtFYfvPnGNj5z4w++wOcXNlpYrhjEx1Xv4/Eqmvm7Pgy65L/22Fk3xTX\nasjF+pdxjPcsnpmlM0ULN9K44UbGdv+olDaiNvCFJe8gpdYe+D548iTqE4LPvPiK+ermectwLscf\n79jJ+BTLDE6k0/zR9h185/pXok8hTrZc9Ma6RIjmi950Vn093wm3ribafQmZk09NWy7UsnpWBIi/\nfepp/vPQYe9NccmEBs9HMlxCfb8txp80Q1iptUrHa1NUpZXyfGVq5UE55GhxWVDNPF+ZWu1XtCmm\nyStkF4517J4PTztQNbIrWbr/H8oJ0tcdGaos+95W1HX1WzrMNnLCIfvRAch5SyZkYemEFJ44IYUD\nIRfjT5shIcF1sB7Yi/PwkYKo4pa3XmxU33sHyziFafTjCTMaiqtjJR4nll9C1GpH4iJdA+JtaFvC\nBQHFQZaEk4JYUkx37YKY4hTEFNtzKF1Y0ihLZRyk6xa2tq9dt2SpJ6vEmVL7p8Gxuz+M0bQULdaG\nFm1Fj7Whxb19LdaGGmn0LB0DAgIWLYEIsYB84hOfmDLv6u8+gaHAqrjO2rjO2oTB2oROZ1idcVB5\nJpypWfhCH/N02/jwxvX0JZMcTNc2VX5zTzc3LPM8xfdVhwcrLsUtH50W4xJa2i9hQ7uXYtkZxjLH\nKyJ1HBl6nJzlrcRXhEZjdCnN0RUsj62gObqCpvYVxEMrSCQaSKVSWK5LyrZJ2nZpm7QK2+LLtDie\nyfK8r4xdw+pCE6Lkv6LW0pBwKMSO/lMzXrcx0+Tu51/gpW2tk/JO9zOoNzzbpOtfZM0baVj5OuyB\n55BOHiW2BK15NSkgNVWdWehX6CGIzNC8szPLqcuzuHUaJizEd6De68+6t6EcewI36QlrP09cMaUA\nUeQXJ3p5XWsLK6L1LZE6nX7NBYvx+gP8dGx8SgGiyPFUmh88t5vfaq89GyiNFejdL8Y6ObWDUb3r\nMpLhVaT6+oLr76P6N0jd9NvQv9vzx1ILoaBvuW3q36w62ZvPlwWIKh7oHuYdB7pnbMPaCGOto3Uf\nc9rrUWsVUg0Ndt7+A1bcBIcequpQGTNyhHziGKHk8pr5RZwOGIgPQ1Xz8/0d0H4HYv+qIvIqAr1y\nBZgO6bdASsdbYoMOV11EvDeEfnCaqEXTkau0ZpS5VWRe3IG5srKY//rP5zSYc+CHjD/+tTpKCpxQ\nC5nxAWT/Htzc6OSQrUJFCTcjIk0o4RaUSDMi3IwSaUEpbEW4iYa2ZaSzZ+eIfC5/gwICzmcCEeI0\niUajKIoy6QcklUpNso44G770ivU8crSPA0mLJ0by3HsyA0BcE6yO66xLGKxJ6KxL6DQbgflzvTTo\nOn/5os08ODjI/acGOJ7NognBpU2NvKazky2NlWbmYtwLrWY8CUoS3DBYF0P+KnBrRCvTtSjtDRto\nb6i0gMmZ454wkTnKaPooY5ljHB95HMvxxBBNCdOaWEVDeBlNBXGiLbaCZZGmukSnYvSRslhhlQWL\nKgHjSDpTEjjSM1hA+PmHAwf56uEjKBQnuDxjYFVRkFIW0n0rhoXwJsYEKMXVvQJURS2t61cQFW0J\nAeF9BxACLNP00kVx1a+vrABBDEEckbZRBvYBAqWQXzQgUnz1/W0V++ZPCxkGlmVNeR6hPoG63rMC\nFnhpL+9todnUuXfZoBefHXCeBZti++W2ik5M/e1HJpLk8/lCflUfa56HmFQ2aprksrlSfnUdpfAY\nWawTk5AtOIWbfG2BTAYFGBFh+K2/JLf7e5i9O3govrWu++Rn/ae4bfmyirTSWMb/kF01kHBMk1TB\nJ8kkVwm+8rWGH9O1W3H8qsSUqpLO5aYuV9Fu7fajQpDOlh0XyhqV/e1G8SzqqlxFVLT9oyNHJ51D\nLX526hQrY9GK+wx8q/Mv+yOyxnewT+wA6SCQhclnQajnSqJbf48xy3MCaJkm2cL1998bUL53yxPU\n/vu0PFNd/f58QW/fTOK3PknqkS8isyMVeSLUSOzy92N0n70F0A+Pn5gy72BjhsfaxrliaOoIFVJA\n7tqz7sa8ciCV4r5TAxzLeP/FW5saua25mdbwZKfV+pKLiF72XjK7vurzDVNGbVhG/pZGjO/g+WWs\ngVQg++rZPoszw14Dzp9GcX6RQX/KC4ohw2BuhfzV4LZXVVAVUm/ZgPHMIKFdp9D6vedC2RaF8RzC\nqj+8qHR17PY2zG2zeEJnSWzdjYw/8S/M5BRZX3Y1iWv+rCJNOiZubgyZHcHNjeJmR3FzI8jC1h45\n6L3PjVXcO+OAMBIFkcITKzzRosn33hMt0CLn3W9bQMBCImQtz4sBJT71qU9NCtH5ta99jaVLl3LT\nTTcB3gDwi1/8Ii95yUu45prZMQeXUlbMAiQtl4MpiwNJkwNJiwNJi7HCH06LobA2UbCWiOusSejE\ntNMzQwtmwcoUFeiBR/uI3ek9GFQjNci8DawXnU5vq9qQkow5VBAljpI0TzI0foCxzHEc15sFDWkN\nNMWW0xxdQXNsJU3RFTTFlhPSzl7wOjK0nd+c+A0/yL6irvKXNzWxqSHhrb6V5ZW4uq6TM02k9AZR\nEkqO4lxfWjFf03XyplmRJgtlQRKORHClJJPNlvJdf1kXFBdUF4StoLmgOALVEagu3tb30lyB6oDm\nKKV8zfHqeWkCzQFNKqi2V14v1NMcgeYqXpop0KX3Xne9/EZL56+3HmRH56h3PgJcRSJ1Cn32rViW\nkvofEQMCzm0qLOr9op6X4BNOZMm3gT/fb/Xvd11YjDZRfK8oSkn5mZTvE/SK+aqqIl3XV7ZSBBWA\nYRieMGOZZUEHEEikOQFWBiElih5FCTd5a+R9fcXXFhSEQJ9oUxRxlNLRvfI7hkdqWrQVCdkKbz/Y\nxdqJKDFLJWZrxC3V23c1zNcLzNPUQhbqf3g8meSfDh7iwcGhSfmGovCJy7axRas9wWKPHCC3/16s\nk48i7TxqoovQ6lcRWvVKhB5B2wPR/wSlyuDRjUL2FrCmcDO14M9BDlNG8KqJ43o+TZsayBwZJHrf\nEfSjEzNWk66GvWQj6d+JIWsYrc1ojThHxONxTv3qb73wwlOh6DRc/zdoLWunLjMN0nWQZrIkUhgy\nQ3asH7cgXniixShudgScKgsJNVQQJJoRRYuKcDOR5i5MES3nhaZfClLvc2ggeASc7wSWEDUwTZOR\nkZGSZ+vR0VH6+/uJRCI0NjZy1VVXcffdd9PV1VUK0WlZVilaxlyQ0BUuaQ5xSbO3rlFKybDpVogS\n/3U8Rdbx+twdUVmbMFgT96wlVsZ1jMC/RN3InEvsW7UFCABhQ/S7kPwQuJNXKNSFEIJYqJ1YqJ2l\nLZeX/phc6ZDK9TOaPloSKPrGnmFv309L4UqjRhvNsRU0RVeUtk3RZWhqaIajejxx5C6ePf49JBBR\nLyUrplhrLEGVAsMV/D8dK+jUwwgbsPGifzkQIUIun/Xe+/MK+8IpbAvphtCxslapvj9P2KArOlgS\nO2fXrI9D0SfZaSMFUHQurlXuK4aCI1zvfagyT2pg7Ko9u/ZnT6+Bp8vvrU2Qfuc0ffAJE7FYjGQq\nVRJgqoULt0rE8As3xbLRaIxUOo3L1MKPW4zMUBB5MplMhfDjr9Pc0oKUMDQ8XDi2xJGSv9m7vy4R\nZUtDAzd1dgD4hm9l/Cn+Z6xIOEI2l62oI2pU8s/219O2qFGimBaJRMjlJn/JpztGdduRSISsLzzh\nVOdXJBqJlixRpmr3CwcO0Z+dOSzk+niM31+5ovA5Fz7DkoVF2Waj+DmX74lCOt4bCYTDYbK5bOle\nKdWvarvsx7+yPelra3Jfatc3jBC5fL50f5fLenuuv35R2KSoOXjH1wshN11/ff/5lvro5RdF0Ipj\nVX1nItEIUkImm6n47gFIo73cvxmOVXzvIr3++a8Vxe94uf7kKB+V5DWXuzbUjvAEEHYVoo+rRDWV\nmKoVtipRTStFVYlpGlG1XKZdAmaeaCGE9nyFnb7r6LGaAgSA6bp86vEn+NSmjZOsEwG0lrXEOnBX\nfwAAIABJREFUX/KhKdu2N8LER0HfDVrBqMheWhAf9Nno/Rxxukatanmg67ZFSP3OJpShLFpfCiS4\nUQ394DjG7mFEzkbqBk5HO9y0glTHaTjFnEei296DtHOYR2qEGNYixK/+0zMWIADPKWa4CSXcBKzy\nLJhrCAJSSrCzJbHCzY4ifRYWbnYUe+IEbnaErJmsPkjhGM0lywpR3I+0kG/uxpFhT7BQF84vSUDA\nQhOIEDU4efIkd955pzdLIwT3338/AFu3buXWW29ly5YtZDIZHnzwQdLpNJ2dndx2223EpvFUPtsI\nIWgLqbSFIlzZ5kXdcKWkL+uwP2lyMGmxP2WxYzCLLUEVsCKmsSZuFKwmdJZGNdRZeuA4nrY4mrZR\nBGxqNM75JSLOzuykWZRqhA3GI5B7zWweGBRHpVH20Gj0sEq92rPfbgLXsklnBkinB8ikh8iMDpHL\njDFsPs2Y+zyqqxNTW4mpbUSVVqJKM2GlkRBxhKOUBvO57Chrxrew3r0U1dV5k9uEIxvRXQXdEeiu\nguF6W90VJXN+fjpVp7PUssuQAu+hqmowLwwHtSAGoHppqCBDIGOgJDTQBJZpl+v7y9YSEabL89Wf\n7iEvHo9OOzsh8mA8O3X9ItbG6fP9ZvOaokzpWLBe4tEIqXpCKxbLx+OkpnEw2tXhCQh9VW2+pKWF\nnSMjtapUcGt3F5c1T+1AL227DOQcdAV6ImXHqQs+C3k2bej1j2zi8TipKWZ4i7x+5Qq++sKeGdt6\nbWcnG2fJy/o5ff3n2CfEfHHHnr08Mzo2Y7kbOjp4Q3cXaccL35q2HW/fdkrvM4633G7MsjmZy5Xz\nHKdm1CYoiGqqJ1xUihUqUZ+oUcyLaZ6w0YZAmCYxVcVQlBlncMdNk5/N4IvIkZLv9/bWFCHqQgNr\nq/e6kHDbIpht5Uhs9tpmsq9aWVEmHo/UHHjPNgP5PD/rP8X24RFStk2rYfCK9jau7+ggodcefghF\nJX7lh7E3vJ78gZ/hTJxAqAZ612UYq16JYszPc7YQAvQoqh5FbeiZtmwsEmJi6ERBpCgvB5FFsWLs\nMDK7Czc3CtLBf+WFEfcEipK/iiZEpIWx8dU0b7ppbk8yIGCBCUSIGqxcuZJPfepT05Z58YtfzItf\n/OL56VCdKELQE9XoiWq8vOCvwHIlR9MWB5MWB1IWeyZMftHvze6EFeE5vkzobGmXLNUdOkKn5/jy\nQNLkzkMT7JkoO+zSBLy4Ncy71zTQdI6KEc7juZkLAcbjgIE3Q181o++fxS/tT5dnp2iadiJMo4lu\noLZzMld1cBQHRzGxRR5byWMro+SVQdAUFF1F1Q1SzhCWmiOnJ3EUC1s5zEl1BWNqM5biYioSS3Wx\nFImluMTDGm9c3k00pJXFAN/gPtoQJZPPTBYBik4RqphpsN/Y5Q1gh/sy012MeSd/9cwihBv11vOe\nj9zc1cnDIyNMd4v2RMJc2lR7zXpf1uZ7x1LsHMxiFRpZElZ5dVeU1/TMn4B7LnDLqpV89+ChaZ1T\ndoVDXNXaMn+dCphzXre0py4R4pbuTroik30m1IOUEtN1SRfECmkYDKeSBeHCIW3bFUJGxnEYzJtk\nnCxpxyZtO2QdZ8rfAVUIT6AoCBhRVSPmEzJimsqJvIlVx0rgp8cnGM6btIaC2eJzjSfHxvibvfvJ\nuWX7uUw2y7eOHefe/lPcsWkjy6KRKetrzWvQrvjAfHT1rBGqjhprh1i1E49KpHSRZpKwyJMePuGJ\nFUXBIjuCmxnEHtmHmx2l96lsIEIEnPcEPiEWKclkkrn6aDK2y4HxHPvG8+wdz7FvPMeprBceqUFX\n2NAUZn1jmPWNIdY3hmkO1daqXhjN8meP9ZJ3avezO6rzhSuX0jRF/cWKEALlc2nE0ZmNzyVAoyjN\nvKOV96VWO/2MyqjTl0Flku13Nj/GcOowI6nDDCfLW9OebOIhgeNs4ZB6JePCmwWMK3neuHIjb1i+\njPhpzPSeLUURbDH+NIlfmqj31B4YyhA474nAunNTeCsy3fX/4fET/OOevTUHIO3hEH+zbRtLY5MX\nGR+cyPPRR0+QmsJx2tVLYvzPS7tmzTLrXEcIwQtj4/zPJ59iwpockaErEuGvt11C9xlEIQmYmYX6\nDXKk5BNPPs1jVeG+/bxlxXLes37dPPZqMq6UZAuCRdqyvW3R2bF/35fnf42Z5rS+L/wogK4ontWY\nEGiKgqYIdKEU0gWaUNAVUShTI81fV4jJ7RXSdF/b5TYq03RFqTqet52vZSzzwdne//3ZLH+w8xFy\n0zi+7gyH+cY1V2GcpSXg+Yq0szS21PB+HhBwHhGIEIuUaseUc42th3nm1Bj7kwWriaTFhO0NGNpC\nqreEo2A1sSauE9EU/viJQY5lpo/tfENnlPeuqz0zuphNcfP/NIpbhzWE0wnJP6z78Gfdt7PFtDN8\nZ+dbpy1jYeCisa71Uq7b/OcztjnvITrniHrPQ9sLoe2gHfR8U0gdzIshf23tiCmzcczZbONsr/++\nZIp7+/t5ZGSUvOvSHjK4oaODVy3poKGGWCWl5ENPDHIyO/2Skfdvbue61vkVLBfj9YfyZ7Dv2HEe\nGBzkoaFhkrZNs6HzivZ2XtbWSmiaJTVnQrAco8xC/gaNTEzwzSNHeWBgENP3eBbXNN7Q3cUbe2YO\n03m6x5zvz+CnQ8N8df+Busq+Y9lSoqqKLSWW62JL6b0K+5aU2K7Elm5lGbdQTro19yvamoXHYAV8\ngoYnYGhClAWMwr4mCnmKQBeCsGGA41SkF/f14n5R7Ci1oVTsJ6JR7Hyuor5e0VZZbFGEmPP/4DuP\nHOOeOup+aO0aXu4LMRz8BpUJHFMGXAicW1PUAXNGU0hjW0uYbS2eiaeUksG8U3J6eSBp8f1jKXKu\nF+KtLaQymJ95HfpvBrO8c1WC6GlG61hotGsjmHWIEPnL5qEzs4ihRYkaLWTMqdf265iAiV6nk8sL\nDXuD9xJZIO/5sah2duZKeV7NjPlZn4izPuE5BqvnPJ8ZM2cUIAB+dHSM61rbZix3IZHQNW7p7uKW\n7iBm/IWCoSi8d/Uq3rFsGY+OjpIurKW/oqX5vJk1/q0lHXx9/4EZHd2ujcV4y9Lp1+PPBrIgRISj\nUcaSqZKgYbsSS/pFjaLQISeXceUkgWSyWFJZP+e6ZE2TvGVPLZDMsliiCk/8UGsIGkWxJLJ3P4aq\n4JrWJOGkWkQpiht+seSXAwN19eWhoeEKESIgIODCIhAhAmoihKAjrNER1ri63Vu350hJb8bmQNLi\nF/2ZukSInCN5ftxkW0vonBqUKReFsJeDdmzqMk4LmOeYCAGwrvNVPH3s32csd3DgAcazvbyo5xZW\ntF2DIs7tZQazjYwAviWth1IWP+lN8/BwjpwjaTUUruuM8qqu6DnrG2Uman2nXSnJOZKMI8naLg/2\n1+fb43jaoj9r0RlZzO7rAwLmh4Su8cqO6deYn6ssiUS4qrWF7cPTO7q9tWd+xDdRGJhHNA1nCoeJ\nc8XpzKJL6Rc6ygKJEY4wkU5NLZa4BRGksK8aBulcrkLg8FuQ6OEwlnRJOm5JLLFdp0ogmUZ4qVMs\nSTvTW9IGBASc3wQiREDdqEKwPKazPKYzYbnsS05eq1yLv35+FE1Ai6HSElJoMVRaQyrdCYs4Ni0h\n732zoSyaNeFCEaTfBbF/Be3I5HynHdK/C5yZb7AFZVP36zh46gFS+am9k2/seh09zdt4vvdu/nvP\n3xALtbOp+2bWdd5ISKsVC+PC5oH+DF/ZP14xszdsunzvWIpf9Gf45JYWlsUW/+DakZKJvE3KcjiS\nssjYLllHknFc0rYkY3v72cI2U7GVpfJnOl/3wceHSOgKTbpCk6HQZKg06QqNhkKzodCkqzQaXn5C\nP7/WYQcEXEh8YM1qRk2L55PJmvnv2byJa840MsZ5SlEs0aEi0lM8FiUl6wmgXCg/x8sxbt/1FP35\n/IzlmvXA4WhAwIVMIEIEnBHrG+obUBkCbl/fSNqWDJsOw3mXkbzD4bTFSF+GvFserihAk6GUxIrW\nkEqroXoiheG9bwmpGMr8DDxkFFLvAfUQGE+CMuHNfptbPXN8aljGnshYjJsujYbC0ujiHHSG9UZe\ndfFn+dULf8VwqnJdriI0NnW/jstXvRshFJa1vpjh1CGe772HXUe+xVPH/p11S25gc/frSUQ6F+gM\nFhcHk9YkAcLPqOnyV7tH+Ycr2udUZDPdsgiQtisFgmxBIMg4ElOkGc+ZZAvvM6WtLHwf+2u2rwmI\nqgoRTRBVBVFNIaoJluhaxfuo6m0jhbSnR/J87/jMM30hRfDOVQnStsuY6TJquQzmvJDD46bXRz8K\nlASJRsMTMRv1snDhiRiecBHTRLC+NiBgERFRVf7X5o3sGB7hvlMDHMtm0YTgksZGbtuymS0tLfPu\nkyNgdriuo53vHD8xY7lXdARLMQICLmQCESLgjNjcGGJZVOP4DI4pX7Ykyks7antwj8VinBpLFsQJ\nhxHT9bZ5h2HTZfeYybDpkLYrBx8JTZQEic5YmoTi0lqwrmgtWFrMpg8KZzVkV09fZudglv86keJQ\nqnw9Vsc1bl0aLy1nWUwkwku4+dL/TdI6zJ7jD2A7OeLhJaxZ8kqiRnNF2db4al664cNctup32XPy\nXvb2/ZQ9J3/MstaX8KKeW4nFFleo2vnmJyfTM65tHsg7PDKUq3kvSCnJuQUrg4IgUBQN0hVCQrXl\nQSHdHSBtOdjTmB8YCiVxIG5ohIUkqiq0hjyhwBMMPAGhp72FuK6SHR8t1YmqCobCGQ3kV8Q0fnwy\nTXaKKDpFXrWsgVd3Tx3tIe9IxiyHcbMoUpT3xyyH3ozNbstlzHQwqz4QTXgCZ6OuVogTSxI2Ydei\nySdiRNRAsAgImA80ReG32tv4rSq/AF0tQejZc5kbl3Twk/5TjNWI7FNkTSzGZU1N89irgICAxUYg\nQgScMbeva+R/PTtSYc3gpzOs8vYVU5vuCyGI6wpxXWHFNKbqWcdlJO8yUhArhn37+8bzDGYtxqtC\n/0VUUbCiKFhQGJ5A0dMoiLoWrSGVxCzNjv7wRIpvHZ5sUnooZfOFPWMM5h1uWbo4lzB0NV9EQl9V\nV9mo0cK2le/k4mVv4eDAr3i+925++sxH6Ti6gY2dr2dl2zUoyvnxk+KtvQVbShy3sJUU1sBWvt85\nmK2rzW8dnuC/B7JVFgqnyNjutCJGRK20PIioCg26QldEI6oJmiJhNNcqWR4Uy8ZUQaTwXvNZD81s\niuuJUH3u5FCuZ0JEVXjHygTfODgxZZkWQ+Ftq1vAntoZbEgVLFE1lsywBEoWfFKMWQWBwnRK++OW\nw5jpcjRl87RlMn4ijVW1ftlQoMknVpSEi6KVhW8/pAZiRUBAQICfRl3njk0b+fQLexipIUSsjkX5\n+MYNwXK6gIALnCBE5yIlmUzOe4zyM2HvWI6v7hlk92h58KAKuHpJnNs3t9MSmp9Bqel4yzyGcnbl\nK1/eH8nZFYM9XRG0hTXaQpq3Lb4iWim9KaROa0J/NJnnfQ8dm3YNvAC+fO1yVibOr2gTUrocG3qM\npw7/B8eHnyAebufiFW9k87LXEdYTSClxJVhS4riekyxHUtiW35ecW7myNMgv5vvTKspJie3ia6ew\ndcvHK7dJRT1/mVLZ6jJz8NVL6AqbmyPENMUTCYovXSGqqeU0vZwX1ubfT8rZxoifih8fHePO/cOk\nqgTDjU1hPrq1k64FWL4kpbd0ZTTvMJK3GTMdRvM2I3mHsbzDqGkzmvfSRk2Har01qio0hVSaQyrN\nhuZtQ4WtUd5vMlQMtX7rrLn6DE4XV0r2jedJWw4dEZ1l8QtjDfdiuf4XKsH1X1hm6/rnHYcH+0/x\nq1OnSFs2raEQN3Z38ZL2tkXj/2uxIoQgkUgsdDcCAuaUQIRYpEgp53U95NnGSj6WtjiatlEEbG40\naK4jGsB8x2d2pMTSIxwfnZhkUTGSd0vLQvxm7QrQ7PNHUbKuKCz/+Hlfml8PzhzK81VdUf5gbeMZ\nnYtbmJV3fLPwxQG4LcsDdMe/rU6X5YF2uZxE0Q0y+Xy5veLAXla+94sAji/PlhIXQd62yDt5LNfB\nRQM0HGYvIoTAM6lXFYEmKIQD85ylagJfuDEvT1UKW0HNdK2QrhbThSAWDuFYZv3HKdT/i2eHGbdm\n/hm9oTPKe9dV3gOLMUb52Tolm468I9k5lKU3a2MIwaUtIdYmjLr6NReczjFdKUnZ0rOsMN2CdYVD\nBo2BdI5xn8XFhOVOEiZjmqBJV2iJGCQU6S3/qPBf4e036ArLerqBufkMpsJ/LaSU/Lg3zU9OVkZB\nWp/QeeuKBJc0z46guhjvf5jb78B0FPuWtl2OZ2wEsDymETkNAetMjzmfbSzkb9B0VPfLlZJHh3Pc\n35fhcMpCEYIXNRq8ujvK5sbz9zuwWK7/6XI0bfHwUI6MLWkLKby0IzJjZKrFeP3B+wyCZYEB5zvn\nh+10wIJTjJqxmFGFoDGsEU4YrJtCYJZSEm3tYCBjsrf3FMOmU7EU5HjaYth0yZ3mVPl/n8owkHMq\nBvRFccApDeYHMR23VKY40K/f5/XMVA/GdSWHImQ5fdIA3EsLKaJk1l/dRiRkIG0LVSSQbo7h5AsM\nTTyL46Zpi61gecultMaWlQb08WgEK5ebYqAv6O5cgq4IhgZOlY4/H7MmZ/owckNnjO/X4Xjxus7F\n5xtkvgmpgpcvmdrvw2JGEYIGXdCgKyyPldNr3TeOlExYruezoiBWFIWLlCsYypgcy9iMmw7JGs48\nmkJDNIc04oo7aRlIo28/oc+NpcyX9o/z4KnJy4z2JS0++9wIH9zQxEs7gvt5thnO2Xxj3xjbB3Ol\nZY5RVfCyjghvXZEgoc+dGBFQieVKPvfCKE+M+KM8SHYM5dgxlOPmnhi/uzqI3rEYGDcd/m7vGM+M\nmRXp/3Ykyau6orxrdUNgeREQsAgJRIiAAB9CCJpCGk0hjURu6sXnGdtzovnp50YYqfaCVwMJ6ApE\nhOKbnS/P1KsCoqEQrmVWztBPMXNfFANU3wBer3qvVR1HFZMdC87+LEACaMd2XsKhwV/xfO89DB//\nNsTXsLnnVla2XEtjQ4xUamoRpy3iiVmZWXQuOpe8ujvKL/ozjFlT3weXtYRYl7gwTNkDvO9fs6HW\ntAir/s5ZridYFB1sjpkuTijGSM7i5FiS4bzLwZTF2BQRQhp8lhSNVVFB/P4r4nX6wHl0KFdTgCji\nAl/eP8YlzaFgUDyLDOcdPvHYcQZylc6eM47kp30Znh0z+fTW1uCazxN3HZqoEiAq+VFvmq6Iyo1d\nsSnLBMw9WcflL54b4Wh6spN0W8K9J70obO9bFzjBDAhYbAQiREDAGRAtrOvf0GCwc2jm5RiXNof5\n/zY3T1tmIczR5wpNDbG+81WsW3IjJ8d2sbv3Hn6z9/M8cfibXLzyTaxquY6Qfn6sd2wyVO64qIW/\n2j3KgM90vchlLSE+vDF4AAqoja6IQmQfFfAEuK4uL/xttTm06UrGTYdR02W8huPNUzmbvRPefq7K\ngYUmioJFZQjTRt2LDNJlqhi2zb0nZ/4NMl148FSG1y9Sh7vnIl87MD5JgPBzImvzrcMTfGB98Fsy\n1yQtlwdOZWYs96PeNDd0RgOz+QXkgf5sTQHCzy/6s7y2O8ayRW6tGxBwoRGIEAEBZ8GNXdG6RIgb\nu85NE/SzRQhBT/Nl9DRfxmj6KM+fvIfHDtzJ43yLtUuuZ1P362mM9ix0N8+aZTGdv7+8nUeHc96a\nVEfSHlK5rjMSWEAEzBqGImgPa7TPECEEvBlCfwjTMZ9wMWo6HM/YPDvmiRieS5Ox0+rLz05mGDNd\nFIH3QqAIEL796jwFb1lLMT0y7mLm84X04kv43pf3RVXdYpsxN08+a5XziuVrtCMEqLZL3pG+ds4s\n9OxsMpCz2TXNrHuR7YNZ3rWq4by2hvAcGntOjW1XIgFXgsTbl5JSGnh+GyRMypupvOvLk4XyYTtH\nJmPy2HBuUpjfWvRlHX7en2FpVAMEAu8+BW/H/14I335hTwiIOjmyWctL8dXx35FeXVFsFv/tKoCY\nYpHJ2b56ouJ41fWsvE3GdAptl9stbpOmjUCQsd1Sv0pt+PouarRdLjc/36tf9M8sFhXL/f6a6f1y\nBQQEzC+BCBEQcBZc1BTi6rYwO6YRIq5sC3NRUzAQbY6t4Jp1f8hLN9/OrgPfZ0/fvezp+wnLWq5g\nc8+tdDZetOCDgbNBUwRXt0e4uj1YKx+w8ERUhUhEYSZXJFJKMo7E1ML0jSe545mRaaP9FBm3XB4f\nyeFKb4DnIn37lAaSxX1Z2sfX/vhZnOGZcmpSiidwlEUSVTmFKgRSulOKIhXiyhQiifCJItUiib/u\nQM6py/eP6cIX94zSGlJL11FK/0C9clA9Oc37LPCVV5QxbMeZorz0Dd4r0/3HRAziurKyDd+26KZ1\n2j768qC//o9zVhk+7RpfPTB16OH5Y3CW25v8HTkbaosqvryiyCEESCYJMtV1ym0KJuz6vGb1Zidb\nKQYEBCwsgQgREHCWfGhjE82Hkvy8P10xe2IocH1nlHetajinB9ezTTTUzCUrfpsty97E4YH/Znfv\n3dz37Mdoia1mc88tdHS8DVUNzCYDAuYDIQQxTbAkbtBMiFVxnUMpa8Z6r+2J8o6VZ+aYT0qJC0Rj\ncSaSqUoBwzeo9osZpTQpcXz7kUiUVCZTel+c4a4QQnwiiREKk8nlarZbFEniiQSuhLGJiYq60rc/\nlcBSbMeR5fP090FKCg6HyzP+yToHUgBDeQfTlYjizLsoiyjgiR3FdAUvr+gPqDijXiwvAF3XcOzy\n+3KbotymL81/vGLZkGFgmdb0bfjKh40QpmlOWb65yVtyMjE+Vigzxbn6+lPOE1VtFssL33nUvl6x\naJRcJsOukTzfPpKs6/P4883NdEW0CuFOFj5nb98nrvgEuOI2EomQzmTKZaW/ncp6sti6rGwjHImQ\nyWQr6xTbKHyf/O2Hw2GyuVy5XKl9L6WpqRkJjI6OlutWnU+xH5XnTUWarKgnS2mV51OuEwqFyOfz\n5b7UKOc/bwl892iyrpDaWvAIFhCw6AhEiICAs0QVgt9f08Cbl8d5eCjHhOXSoCtc2RY+r81mzxZN\nMVjXeQNrl1xP39jT7O79Lx7a90WeOvZttq19K93xawnrgffxgID55MauKF/ZP72FgoIXFeZMEUKg\n4vnDCKnV86SnRzweJqVNvya8snycVGr643V1LQGgr282YxNNzf6kyZ8/NfMsvAA+9qIWOiOz9+i2\nGEMUdnW1AtDXZ05ZZi6Ix0OkhEV7WOU/j6cmOYKtZnVc57LWOtZGTXvMMCm1/vu3dhtRUqH671Xv\n+k+d39Xl+a/q02ZeajqbnMm9eCBp8ujwzEuZLp2lsMIBAQGzh5BS1qEhBsw3yWSS4KNZGIpWC8H1\nn39GUkd5+sj32dN7HwLBhp4buWTlm2mOr1jorl0wBPf/wrOQn4HlSj72aC/Pjk4dIeN31rbwznWt\n89ir+WUhrv//u/0YByamH0xd1hblL684933ozMRi+A36t/3DfPvAyLRlPnFpF9d0nn/OWRfD9a+X\np4czfPTR3mnLxDSFf33FKiLnSMQt8D6DROL8cN4dEDAVgQixSJFSTvKMPpcsRGSGxTgDA9DV1QVM\n9kw/1wSfgUdXVxeZ/Ci/fupOXjj5Y3LWGEubL2dzz610NW2ds6UtwfX3CO7/uW1jsf4G+fuVc1y+\ncXCC3wxksX1PCA2awhuWx7i5Z3YGXsH1L/PCuMlfPDeC5dZ+JIuogk9vbWXlLHv4X4yfwWL4DZJS\n8i+HJvjpycmODxXg3WsaeHX32YfnDK5/mTO9Ft89muR7x2rXMxT4080tXDKFJcRivP7gfQbBMt6A\n851gOUZAQMCiIxpqZuvyt7Nl6Zs4PPhrdvfezf3PfZzm2Eo299zK6vaXoSqB34iAgLkgrCp8YH0T\nt61M8NhInqzt0hZSubw1jK4ED8ZzwaZGg89c3s3fPdvPySoneitjGreva5p1ASJgaoQQ/I81jbyy\nM8r9JzMcSXs+LzY3GtzQGaM9rC50FwMKvG1FguVRjR/1ptmX9PzZaAKuaA3zhmVxVseD701AwGIk\nECECAgIWLaqis3bJK1nTcR3948+wu/dutu/73zxx+E42db+WDZ2vIWwEYbcCAuaCRkPl+s4LM7zw\nQrC1NcrfX97Bs2N5DiS9sI0bGw02NgTRlRaKlTGd96wL/mMWO1e1R7iqPcJw3iHruDTpKvHAJ1dA\nwKImECECAgIWPUIIupq20tW0lfHMCZ7vvYdnjn+fZ45/jzUdr2Bz9y00xZYvdDcDAgICzpqLmkJc\n1BQ40gsIOF1aQyoQWKkEBJwLBDJhQEDAOUVjdClXrfsAb3nxN9m67O0cH3mMu3e9n58/dwe9o7vO\nCWdaAQEBAQEBAQEB5y+rVq1ix44dZ93O0aNH0fXzb1lRIEIEBAScW9g2yvgYkbzg4uVv5c1XfIOX\nbvgTsuYYP3/uk9yz6wPs678f253f8G4BAQEBAQEBAQEXHnfddRc33HDDnLV/PjoqDZZjBAQEnBOI\n1ASRx3ZivPAsSt4LZWe3d5Dfejlrtryc1e0vp3/8WZ7vvYcd+/+BXUfuYkPXa9jY9VoiRtMC9z4g\nICAgICAgIOB8REp5XgoFc0lgCREQcJ6gjI8Rfvg3RH/5UyLbH0QdGljoLs0ayugIjd/5JuGnHi8J\nEADa4ACxX/yE2M9+iAC6mi7mlS/6BG+8/CusbLuW3Sd+wPce/X227/s7RtNHFqz/AQEBAQEBAQEB\niwtFUfjSl77EqlWraGlp4atf/So7d+7kRS96Ea2trXzmM58plXVdlzvuuIOVK1fS1dXFRz7yEVzX\n5fDhw9x+++386le/IpFIcNFFF5XqPPzww2zevJmWlhY++MEPltKllNxxxx0sX76cnp6djn3CAAAg\nAElEQVQePvShD2FZVin/s5/9LJ2dnaxevZof/vCH83Mx5pnAEiIg4FzHton94icYe55D+PwhRB7d\ngblyNembbkWGIwvYwbMn/pMfoKSnjqsd2vMcds8y8hdvA6Ah0sOVa2/n0hW3sa//Pl44+SP2n/o5\n3U2XsrnnVnqatwWKdUBAQEBAQEDABc5DDz3ECy+8wCOPPMJNN93Ea1/7Wnbs2EF/fz+XXnopt912\nGytXruTzn/8827dvZ9euXWiaxhve8Aa+8pWv8P73v5+vfOUr/Nu//Rv3339/Rdv33HMP27dvJ5PJ\nsG3bNt785jfzspe9jK9//ev84Ac/4JFHHiEcDnPzzTfzV3/1V3zyk5/kJz/5CV/+8pfZvn077e3t\nvOlNb1qgKzO3CBl4cVuUJJPJwMHeAlEcnJ4r11/77rdR9j0/Zb7b1YP9e+8D7dzQHKuvvzh2BP2u\nf56xntu+BPt9f1Qzz3FtDvT/iqcO/weDE/tojq3gklVvYUP3DWhq4IXez7l2/5+PBJ/BwhJc/4Ul\nuP4LS3D9Fx4hBIlEYqG7cUGgKApPPvkkW7duBaCzs5OvfOUr3HrrrQBceeWVfOxjH+P1r389mzZt\n4pvf/CZXXnklAPfeey+f+9znePDBB7nrrrsmiRCrVq3iC1/4Am94wxsAeNvb3sY111zDH/7hH3L9\n9dfzzne+k9/93d8F4P777+dDH/oQL7zwAu9+97tZuXIln/zkJwH45S9/yU033YRpnl++zs6NUckF\nSDwep6+vb16Pl0pNPdO8WI/5f9m78zC5rvLA/9+71b70vm/al9ZiyZYsWZZsg42xwTbBLMEJjhkg\n8TAYMzGMmXiehAzLQzIDIRCSHwQSGDCLcYyNHfCKLcuyZFuStcva1bt6X6q6a7nb74/qbnWrW+rW\n0qvez/NIXXXvueeee6v71q23znnPhdYxnvLFxcUAk3r+4eLOh15fS+Q8AQgAtamB9M43SVevuCz7\nvNQ6QsEg8e5usG0U2+r/aYNtodg2eVlZYFm0t7agWBaeA3vHVa/a2kxfQz1OdPT8D6WRdZSsuJaW\nngMcaHiSl/d/k9ff+QGLi29nUcn7KMgpl78BZtbv/3TY52y5Bsn5P0P+Bia2DrkGXd59yvmf2n1O\n5DVITI78/PzBx36/n4KCgmHPB16v2tpabrvtNhRFGQzSlZWVnbfuwsLCwceBQGCwrsbGRioqzkwt\nX1lZSWNjI5D529u4cePguvLy8os9tGlNghBCzGDefW+Pr9yenZhz5p35sG/Z4NgoXR70eCwTCLBG\nBgRGBgpGBg50FELp5Jk6h25vjRJocBxyztPWgRFxkYs5IenkeVcrikJhdBmF0WX0JJo41PhbDjQ8\nyb76x1lYcjMLC99HTnDOxexZCCGEEELMUmVlZfzqV7/iqquuGrHuQof4lpSUUFtbO/i8pqaGkpIS\nIBOEqqurG1w3tNxsIkEIIWYwrbN9XOWM5kayv//t0dedYxtXUUDTcTUNNA1X1zM/tYGfGmg6eDyg\najh+z/DyZ5frf+wNBkmaVn+d2pBtMj/zCgtB12nt6MTVNDyH9hPc8tK4jjP6i59glZZjlldhVlRh\nFxSBOnr+3Yi/mGvn/QVXVf4JR08/zztNz/BOw7MUZ61kaekHKMu+GkWR3L1CCCGEEFe6T3ziEzzy\nyCP86Ec/oqioiJqaGmpqati0aRMFBQXU19dj2zaapo1Z10c/+lG++c1vcsstt+Dz+fjqV7/Kxz72\nMQA+9KEPcf/993PPPfeQn5/P//k//2eiD21KSBBCiBnMHWeeB8fno/c9dwwLCriaRiAcpjeVHr5c\n10DVzvnh/WwX2hXRCIVIn6e82t8N0bEcAFLLVxHYvgXFPP9YuNT8RVil5Ri1J/G/uZXA1pdxvD6s\n8srBoISTnQtnRau9eohlZR9kzcJ7OHDqOQ42PMlLB/6WiL+MpaV3Mr/gXeiab9zHJ4QQQgghpr+z\nezCc7/kXv/hFbNtmw4YNtLe3U1lZycMPPwzAu971LqqqqsjPz6eiooLdu3eft65PfvKT1NfXs3bt\nWhzH4cMf/jBf+tKXALj99tv5i7/4CzZs2EAgEOChhx7i5ZdfvqzHPR1IYsppynVdGQ88AXXMtvHA\nvjdfJ7B17AtTcvkq+m6+/bLs81LruJjxqL4d2wmcpzeE4w/Q88f34WRlZxbYNvrpRvS6Uxi1J9Gb\nGlAcBycYwqyYg1mRCUq4oTODPgba5bouLT2HONjwJLXt2/HoQRYV387ikvcR8Iw+kCRlxTnR8go9\niUY01UN5zjUURpdd8rkYDxkPPLX7nC3XIDn/Z8jfwMTWIdegy7tPOf9Tu8+JugbJDF5itpOeEELM\nYKnlV+F/4zUUyzxnGVdRSK28ehJbdfklr1kHrot/+5YRx2pn5xJ/3x+dCUAAaBpWaTlWaTnJdRvB\nTGM01KHXZoIS3kP7BrcdCEiwqBoYyBuxlMLoUmLJ0xxqeJqDjb9lf/1/MCd/I0tLP0BuaN7grvbW\nPsbeul9hOanBZfvrHycnOJcblzxMxF86cSdGCCGEEEKIGUaCEELMYK4/QPz2DxD6zycyiR/PXg/0\n3fge7PzCkRvPMMk160ktX4Xn0L5MLgxVIz1nHlbFnBFDLEYwPJhV8zCr5pEAlEQfel0NRu1JjJoT\n+PbsxH3mCSIFRYNDN6zScsK+ItbO+zRXVd7D0dPPc7DxtxxveZmi6HKqSz9Ae/wku2t/NuouO3pP\n8Oze/8n7r/oHAt7cy39ChBBCCCGEmIEkCCHEDGfOW0jPR+7Ft2M7nuOHURwHFzAr55K8eh1W5eyZ\n7cH1+UitWnPp9fgDmAuXYC5cAoDa002opQn76Dt4D+7Fv2MbrqZhlZRhllehVcyhuuQulpTeSW3b\nNg40/IaXDn5lzP30pTvY3/AEa+d++pLbLIQQQgghxGwgQQghZgG7qITe93+Q3lQKNdGH6/Ph+vxT\n3awZw4lEcUpK6Z2/GFwXrb0NvfYkRt0p/Du2EXh9M47Hi1VWwaKKOcwr/xKvd/0Hh5qeGbPuY80v\ncXXVfWjqueYhEUIIIYQQ4sohQQghZhOvF8frnepWzGyKgp2Xj52XT2r1WnActOamzNCN2lMEtryE\nYtvc5FWoCs7nWFYHpyJdmLozanVpK05n7ylyQ/Ml0ZQQQgghhLjiSRBCCCHOR1Wxi0uxi0tJXns9\nmCZ6Yx2te58ku9HP+08sREGh2R9nR1EjTcE4cU8aRz0z8dAzu/87uuol7C8m4ish7C8h4i+mIHsu\nBtkEPDkoyvimRBVCCCGEEGImkyCEEEJcCMPAqpxLb3gTTx34Mj5LpywWoaInyvrGcnJSflxcWgK9\nnIp00VLgoXzZh+kxW4glmuhJNnGqdQvxVAuZ1KGgqV7CviIi/mLCvhIi/UGKsK+YoDdPAhRCCCGE\nEGLWUFzXdccuJiZbLBZDXpqpMdBlXs7/1Jgp5991XR7dci9dvXXDlofSHip6olTEolT0RAmZHlxV\nwy2rwJkzD3fOPNyScmxsehJNdPU10N3bQHdfw+DjWOI0LpnhHZpqEA2UDvuXFcz8DPkLUBXtsh7X\nTDn/s5m8BlNLzv/UkvM/teT8Tz1FUQiHw1PdDCEmlAQhpinXdWlqapq0/YVCIeLx+KTt73Lt80Lr\nGE/54uJigEk9/yCvwYCZdP47ek/y3N5HSFk9o66fX/BuNuV9HKMuk09Cr69BTaVwDQ9mWQXq/EX0\nFhZj5xUMm2bUdkziyRZiyUZ6Ek3Ekk30JBrpSTQSTzYPBihURScSKCbkLeof5lFMxF9MxFdK0Jc/\naoBiNp3/6bDP2XINkvN/hvwNTGwdcg26vPuU8z+1+5yoa5DkkBKznQzHEEKIi5QTnMP7r/oW++p/\nzYmWzVhOEoDs4ByWlLyfBYXvwVEUUrl5pK5ak0ly2dKEUXsKo/YU2h+eI2pbOP4AZkUVVnkVZsUc\niGYN9no4m+NYxFOt/QGKRpJWG+2xWuo7dxJvOo3jWgAoikbYV0jYV5wZ3tEfpChS5qM6IVRVLv9C\nCCGEEGLyyV2oEEJcgrC/iOsWPMCauZ+mL9WOrnkJevNGL6yq2EWl2EWlJNduIOT1kjz6TiYoUXcK\nz5Hfo7gudiQrE5SomINZXokbCA6pQs/0dvAXU5p99bBvVRzXpjfVmsk9kWiiJ9lALNFEY+duDid/\nfyZAgUrIV9ifKDMTpAj7S4j4iimw89A0mU5UCCGEELNbVVUVra2taJqG1+vl3e9+N9///veJRqMj\n1q1atYoHHniAO+64Y3B7VVUJBoMoioLrulRXV7N9+/YpPKKZQ4IQQghxGRiab9SeC+ffyMCqmINV\nMYcEoCST6A01gz0lfPt3A2DlFWBVZHpJmKUV4PGMWp2qaIR9RYR9RZRkrxq2znFt+lLtmHTS0nE8\nM7wjdZrT3fs42vwCtpMGQNmlEg0UEzAKBoMUmdk8Sgj5CtHV0fcthBBCCDGa5P/6S7Dsid2JruH7\n6rcuaBNFUXjxxRdZv349iUSCe+65h6985Sv83//7f4eta29v5ze/+Q0f//jH+fu//3v+/M//fHD7\nI0eODA5jEuMnQQghhJgmXJ8Pc94izHmLAFDiMYy6TEDCOHII3643cVUVq6jkzNCNBQvHVbeqaGQ3\ndBHcv4e5J49lelxEc0ituJnENSvpo5eeRCOqp5fOeD1NbUdp6T7IseaXsJ1Ufy0KQW/esMBEZrhH\nZiYPXfNO0JkRQgghxIxl2WBbU92KUQ2kR/T7/dx55508/vjjI9bl5ubyqU99ikQiwSOPPMKnP/3p\nwd4Pkl7x4kgQQgghpik3FCa9ZDnpJcvBdVG7Ovp7SZzEu3sH/jdewzUMQqXlWOVzMCuqsPMLhyW5\nHBB45Xl8b781bJnW3Ulgy0t4Du5F/dCfEMxaMSIpmeu6JNId9PQnx4wlGulJNtEWO8KJllcG82AA\nBDx5mYCEv5hI/1Sj4f4AhaH5JvBMjeS6Lm2xIyTMLnxGhPzwYkn0JYQQQohR9fT08OSTT7Ju3bpz\nlrnzzjt58MEHOXz4MIsXL57E1s0+EoQQQoiZQFFwsnNJZeeSWnl1JsllazPB5kaUo4fxb9tMYMtL\nOD4/ZnlV//CNKpxoNp6De0cEIIbS21sJPvtb4h/82Ci7VQh4cwl4cymKLhu2znVdEmbXYGAik4ui\nkY74cU61bsG0E4NlA54cwr6SYUGKTB6KIgw9cPnOE3C0+UX21f2ankTD4LKwr4hlZXezqPi2y7ov\nIYQQQsxct912G5qm0dPTw8KFC/nhD394zrJFRUUAdHZ2Di6rrq4GMvdLX/7yl/nc5z43sQ2eJSQI\nIYQQM5GqYhcW48xbQHzF1WBZ6E0NGHUn0WtPEfjDs5khF+EIipkeszpPzQnU9ja4gHGNiqIQ8GQT\n8GRTGK0ets51XVJmDz39s3jEEk30JJvo6D3FqbbXMe3ewbI+I4vsUDlBT+Hg8I7MUI8SPBcYoNhT\n+wvernl0xPJY8jTbjn2PeLKZq+fcd0F1CiGEEGJ2evbZZ1m/fj2mafLXf/3X3H777bz11uhf3Az0\nEs3JyRlcdvDgQckJcREkCCGEELOBrmOVV2KVV8J1QCqFUV+D59hhvAf3jqsKz7F3YNnyy9IcRVHw\neaL4PFEKIkuGrXNdl5QV6w9MZIIUCauVjlgdde1vkLJig2V9RvTMNKP+4iG9KUrw6qFh9Xb21owa\ngBhqX/3jVOZdR154fLk0hBBCCDF7DeR0MAxjMPFkR0fHqGWffvpp8vLyWLhw4YjtxYWRIIQQQsxG\nXi/mvIU44ci4gxBqLDZ2octAURR8RiSTqyGSScI5dKrRlBkjluyfZjTRSKw/UNHQuZOk2T1Yj1eP\nDJtmtLnn4Lj2f6jxP9m4SIIQQgghxKTQtWm/D9M0efTRRyksLBzW0wGgo6OD3/zmN/zN3/wNX//6\n1yXH1GUgQQghhJjFnHAEV1VRHGfMsr59u0h9/SRq1Ty8OXlYpeXYufmjJrqcSF4jjNcIj9pbIWXF\niSVO9wcmBoIUTTR27SZpdo2r/paeA5e7yUIIIYQ4hwudOnMyvec970FVVTRNo7q6mieffHLEOo/H\nw6pVq/jxj3/MnXfeObheghEXT4IQQggxi7n+AObcBXiOHT5/OU2n95b3Ee2L4Zw8TmDvLhTHwfH6\nsErKsEorsErLsQqLQZuEbzTOwauH8IbnkxeeP2Ldb3b8V7oTdWPWkTLjtPQcwh+4aiKaKIQQQogZ\n4OTJkxe1boBt25ezOVcUCUIIIcQsl1i7AePUcRTr3HN0J69ZR3rJMvSBKTpra9CbGtEbajEa6vBv\n34JimbiajlVckglIlJRjlpSBxztZh3JeeeGF4wpCmHYfv9vzRbR9HvLCCymMVFMUXUZ+ZDGG5p+E\nlgohhBBCXLkkCCGEELOcXVhM7M6PEPrdb1CTiWHrXEUhuXotifWbhm9keLAqMlN9JgFsG621GaOh\nDr2xDu/et/G/sRVXUbDzC7FKyzFLyrFKy3GDwxNGTpbFxbdzvOWlMcvdvOzLePUwXclj1Lbu5HDT\n79lb9ysUVHJC8yiKVlMQqaYwuhSfEZ2ElgshhBBCXDkUV1J6TkuxWEyyrU6RgfFdcv6nhpz/CWSm\nUffvQTlxFMW2cfMKsFevhazswSLjPv+uC+2tqHU1KLUnUWtPoXRl5s12c3JxyqtwK6pwKqogO3fS\n8kq8vP+bHKh7+pzrF5W8h1tW/tWwZa7r0tlbS2PHHho799LUsY9YshmA7GAlJTkrKMleQUnOCsL+\nwgltP8jfwFST8z+15PxPLTn/U09RFMLh8FQ3Q4gJJUGIacp13cG5aCfD0Mz0M2mfF1rHeMoPzPU7\nmecf5DUYIOd/YuuYyPOvxHswGurRG2rRG+vQWltQACcQzPSUKK3AKinDzi8EVb2k4zgX13XZW/dL\nDjQ8Rdo6U5+hBVlS8n5WVf4JiqKOuc94soXmngM0d2f+DQzzCHrzKYwuozBSTWG0mtL8xfT29o67\nfdP1GiS//2fINWhi65D3gMu7Tzn/U7vPiboGScJDMdvJcAwhhBCXhRuKkF60lPSipQAoySR6Uz16\nQx16Qx2BLS9lemB4PJjFZZm8EqXlWEUll60NiqKwsuJjVJd+kPqOt0iYXfiMCGU5azE037jrCfkK\nCPkKmFdwEwDJdHcmKNFzgObug5xs2YyLg9+TRX54CYXRagoj1eSE5qIqU5e4UwghhBBiupMghJiV\nXAfitTrdhz2YMRXVcAlVWnhXT3XLhLhyuD4f5pz5mHP6Z7KwLPTTjeiNmaCEb8d21Nc346oqbkk5\n/qKBhJdluL5LSxCpa16q8q+/DEeR4fNEqcy7jsq86wAwrT5aYu/QmThKXdvb7Dz5ExzXRNf8FIQX\nZ4IS0WXkhReiq57L1g4hhBBCiJlOghBi1rGTCvXPB0i2DP/1TrbqdOxxKXmXTrD83LMECCEmiK5j\nlVVglVVknjsOWnsrekMtvuYmvIf24d+xDQArN7+/p0RmalAnHJnCho9k6AFKs1ezqHwTy0ri2I5J\nW+xof0+J/eyvf4K3a36Gquj9M3AspTC6jDnea6a66UIIIYQQU0qCEGLWafzDyADEAMdUaHgxQOUH\n4niznUlumRBiGFXFzi/Ezi/ECIWIx2Ko3V2Z4RuNdRh1Nfj27gLAjkSx+mffMEvLcXLyJi3Z5Xho\nqkFhdCmF0aVQ/mEc16azt4aW7gOc7tnP0eYX2Vf/OMoBlexg1eDwjcJoNX5P9tg7EEIIIYSYJSQI\nIWaVRItGX+P5f61dW6Fzv5eijYnzlhNCTDJFwcnKJp2VTbp6RWZRX28mINGfV8Jz+ACK6+L4/P1D\nNzJBCbugCLTpk4tBVTRyQ3PJDc1lSekduK5LLNlIV/IYNS27qO94i0ONmVk8Iv4SCiPLBgMTRW6R\nJCUTQgghxKwlQQgxq/QcM8ZX7rhB4fWJ6fRFqhBiFG4giDl/Meb8xZkF6TR6UwNGQy16Yz3+bZsJ\nWBaurmMVl/YHJSqwikvBc4m5GMw0evNpcB3snDzcYOiiq1IUhYi/lJL8RVRk3wBAb6qN5u4DtPQc\npLn7AEebnwcgdCCf8rzVRD1zKYxWkxWoGJzRQwghhBBippMghJhVrMT4btRdSyHdo+CNygy1Qswo\nHg9W5RysyjmZ57aN1nI601OisQ7vnp3433gNV1GwC4oGh29YJeW4geD49pFKEdi2Gc/BvaipFACu\nqpKet4jEhhtxsnMuy6EEvXnMLbiBuQWZoETKjNHSc5C4XUNd29u8U/8irmvj0UP9OSWqKYhUkxea\nj6rOrLdvJd6Dd99u9JbToChYpRWw9rqpbtbsZpp439mPd/9u1K5OXMPAnLuA5MprcHLzprp1Qggx\n5aqqqmhtbUXTNCKRCHfffTff/va3URSFm266iU9/+tPcc889ADz66KM89NBDPPvsswQCAb7whS+w\nfft2ADZt2sR3v/vdwSluv/zlL/Nv//Zv9PT0UFhYyJe+9CU+8YlPTNlxTkcz6y5GiDHovvHneTj1\n6wh6wMGba+PNtfHl2nhzHdwiV7pCCzFTaBp2cSl2cSmwDlwXtaNtcPiGcewwvl1vAmBn5w4GJKzS\ncgiOEpRIp4g8/rPMh+UhFMfBe/QQRt0pej5y74R8iPMaYcpzr6W4+AMA1NafpC12mObuzNSgu2t+\njuWk0FUv+eFFFPQP38iPLL6g6Ucnm+/Nrfi3vYrinLk+e44fwX19M56bbye9ZNkUtm52UnrjhJ/4\nBXpby5mFyQTanp14971N783vGxzyJIQQE+3gdzfiOuaE7kNRDZY+sOXCtlEUXnzxRdavX8+JEyfY\ntGkTy5cv51Of+tSwcgMBiOeee46VK1fy1ltvcffdd/Ozn/0Mv9/PF77wBe677z6ee+45AD7+8Y/z\n8MMP4/f7OXbsGJs2bWLt2rVUV1dftuOd6SQIIWaV8DyTrkPeMcuFKtNE5pkk2zVS7RrdRzx09GV6\nUdR6E4QKVZSwrz8wYePNclCmz3BzIcS5KApObj6p3HxSKzJz8qqxnkyyy/4hHN79u1EANxwhWFw6\nOAuHnZtPYNurIwIQQ6nJBKEXnqHnj++b8EMxNB/FWSspzloJgONYtMePD87AcajxafbU/gJF0cgN\nzc/kk4hWUxBZitcIT3j7xsO7ZweBra+Muk6xTILP/RbX78esmje5DZvlQs/8x/AAxBCK4xB84ZlM\nj56Fiye5ZUKIK5HrmLj2xAYhLpbrZnpFz507lw0bNrB79+5h688OQACsWbOGNWvWDJb57Gc/yzXX\nnJn9at68M+9pA/WfPHlSghBDSBBCzCqBIht/oUWi+dy/2orqkrs6hS/XITz3zFSdVp9Cql3DMLOI\nn3boqtfpOuABFBTVxZPtDOkxYePNsdEucci5EGLiOeEI6cXVpBdn3vyVZAK9sZ5AazPqyeMENr+I\n4jg4Hi+KNfZNkt7UgNbchF1YPNFNH0ZVdfIji8iPLGJZ2QdxXYeuvlqauw/S3LOfk62bOdDwBABZ\ngcphM3AEvVPQ/d628W9/7bxFFNfFv+1VCUJcRnpjPUZj/XnLKK6Lb9cbEoQQQoh+R44cYcuWLfyP\n//E/Bpc99thjbN++fVgAYjSbN28eEWD4u7/7O77yla/Q19fHNddcw8033zxhbZ+JJAghZp2Sm/uo\nfzZIqn1k1wVFcym+qQ9f7shhG3rARQ9YFBdnkls2NXXgpCHZoZHq0Ei1aSQ7VGLHDFwnM1zDiGSG\ncPiGDOnQA5JnQojpzPX5MecuwF6xing8DpaJfroJzzv78e17e1x1BDa/gF1YjObx4jfNzHShigIK\ngDL43IUh6xRUrxdf2syUUxRcRRlSPrPMPpUFioK3pyfT3iHbD60bBQpRKFDmsFybi5tzBzGnk9Pp\nk5xOn+B061scbvodAGEtlyLfPIq88ynyzydiFGaGnfX/U4IBtETirPoH9jmkDWevZ8hxnHXsxsnj\nqH29Y55L/XQjWmMDTm5u/ws0+N+QF80d/bGSGXow6rqBOtzBomBbqL29o5fvf+x6dHBB7eoY0gx3\n+OOz2qmcve58bT7HOiXgR+vrG1GNMo5thywYHH40FuP4EcxxBN2EEGI2u+2223Ach97eXu6++24+\n85nPDK57+eWXue6661ix4tzD144dO8YjjzzCY489Nmz5ww8/zMMPP8xbb73FH/7wBzyXmix7lpEg\nhJh1dL9LxZ1xYicMug97MOMqtgK92Q5WhYoagJB75v75fFRPpndFoMgeXOY6kOpUM4GJdo1ku0bH\nXi9OOlOh5h/SYyLHwZdnY0QcmYlDiOlKN7DKKgDGHYTQOjtQEglURcHj2JkPgy79Hwrd/ufumQ+Q\nA88Bnztk/ZCyAx9qrf5tAo7DhV42IkApACown169goZwjIZQDw2hAxwNvAkKBEyD0liY0niEsliE\nUCJA9IL3dvlEf/Xji972Qm/rxiqf7v+ZdRFtuVTRSdyX4jjQn3hVCCGuVM8++yzr16/n6aef5nOf\n+xzxeJycnEwC6m984xv86Ec/4r777uMnP/nJiG0bGxu59dZb+drXvsYNN9wwav1r1qzhpz/9KT/4\nwQ+4//77J/RYZhIJQohZSdUgusCkL9/h+b1+WuMaWCqcAE4EyfI73Loswdx8a8y6zqao4Mt1Mr0p\nFmS+RXJdMOOZ4RwDgYmeYx6s3kyeCUV38ebY+PLswZ+ebAf1MuSZiCUVTrTq2I5CTtCmMteWgIcQ\nF8HOzcPVNBTbHrNs/L13YlXOJRQKZXpTjNN4yg9k125qajrzTfeQwMWw5/QHOkYr17++wIUC12WV\n65K2emnuO0pz/DDN0cMcT5zAcS08qp8C/3yKAgso9C0g31uJpugM9ABQzqpzMOBynjbotSfx794x\nrvPSt34Tds6QISNDL2LKkAfK8IU+v59kInFW+XM87i+fSCaHV6wMf5ybkwsKtPHcErkAACAASURB\nVHd0nN2AkdHrIc/dc7V5HNuiKAT8AfoSfaNEyBXcs+tTzn7c/0AB35vb8B0YPqZ5NK6iQKIPfIEx\nywohxGw1kLPhjjvu4KmnnuKrX/0q3/rWtwCIRqM899xzbNy4kQceeIDvfve7g9u1tbVxyy23cP/9\n949IZHk2y7I4duzYxB3EDCRBCDFrNfeo/PzNIGlr5CfyroTKr3cE+OiaXqryxv7AMRZFAU/YxRO2\nCFcNyTORVEi1q4OBib5Gna5DHnAVUFw8WZmeEt5cG7cUnABoY+fVBKAvrfDcfh9Hmg2cM3eoZAds\n3rU4ycIiC8eC2EkDM6ai6i6hSgtPdPwziAhxJXH9AdILluB9Z/95y9lZ2VgVcyanUUOGPJzLWAPA\nhq7XyaI0t5RSbgTActK0xY7QlThKbdvb7O74PZb9BKpikB9elMkrEV1KQXgJhn5hH1at4lJ8+94e\nM6jjhMIk124AdXxTLA/lDYUwLyAI5A2FsMYor/YHgaxA0wW351K4oRD2BRzLuaRWrxlXEAJFwfMv\n/0C4tIJU9QrSC5aAdBcWQkwARTVmxD6+8IUvsHbtWh555JHBZbm5ubzwwgts3LiRSCTC1772NWKx\nGLfeeit33HEHX/ziF0fU88Mf/pAPf/jDRCIRXnnlFX7+85/zi1/84pLbN5tIEELMWi+/4xs1ADHA\ncRVePOTnUxsv/abvXHSfi15qEyw9cxPuWAwZypEJUMROGLRuV4AoRqh/2tA8G19O5qcecId9Bkma\n8Oj2IG3xkV0pOvs0ntgV4K6oiX5Cx0mdubFvfdMlWG5RtCmB7pfcFUKcLXHdDRi1J8+Zy8BVVfpu\nunV847lmAF31UBRdxvzSdSwu+iMc16YjfnJwBo7DTb9nb92vUFDJCc0dTHRZGKnG5zn/4AE3ECS1\nZBm+/XvOWy658pqLCkCI0dl5BaTnLcRz/Mg5y7i6QfdHPk6oLw673iL4/DMEX36O9IIlpKpXYJVW\nzJrfcSHE1LvQqTMni3LWdW7x4sXccMMNfOc73xm2rrS0lBdeeIFNmzaRlZVFYWEhu3fv5ujRo3zv\ne98brKunP5fTM888w5e+9CVM06SiooJvfvOb3HbbbZN3YDOABCHErNTVp3Cybexf79aYRn2HRlnO\npfeGGC9VB3+Bjb9geJ4J3QzRWZcanDa0c79nMICg+Ry8OWeSYO6NabTFNM41hHtBTEFt9DCyz4NC\nb51B3e9UKu6YuOCLEDOVE82i5yP3EnzhGYyGumHr7Owc+m58z6yeyUFVNPLC88kLz6e69C5c16U7\nUU9L9wFO9xygpn0bBxufAiDqLxsyA8cyQr6CEfX13fgetM5OjIbaEeuSmsWepSoH9V+Q2P49PHqQ\nqryNLCq+bWpm85hF4u+9i/Az/4FRc2LEOtfjIfb+u3EKi3FCIeJzFqD2dOM5tA/vgb14D+7FjmRl\nekcsWY4TnYrsGEIIMfFOnBh5jXz66adHLTtv3jwaGhoGn997773nrPfJJ5+89MbNchKEELNSa0zj\nnJ/Qz/K7fX6iAQdNyXwZFwyk0FRIJf1oiouqgqqApoLa/zxT1s0s7388sH2mrDtsG61/+TnLKRAO\ng6/KJDjXRO1vutWrDA7lSLVrxE4adO7zkg/cqUCPDl0GdBuZnz066C4siZ3/mNOdGl0HvUSvv6TT\nLMSs5GTnEPvIvWhtLei1p1BcFyu/AKu86or7dlhRFLIC5WQFyllY/F4A4smW/p4SB2jpOciR088B\nEPTmUxhZSmF0GYWRaqKBchTDQ+zue/Ae3It37060lmZQFFoqojxVtJ243Qn9nU6SZjd7637Focbf\n8q6l/4virHNPhybG4PEQ++DH0GtP4t2/G62rE1c3MOctJFW9AtfnH1bciURJXns9ybUb0Bvq8B7c\ng3/HNgLbXsUsryK1dAXpBYvBmPgu1UIIIWY/xR3IxiGmlVgshrw0F+9IE/xky/iyPpZmu0QDYDvg\nuMN/2g44DtjukMf9zwceZ8pe/g8mwwMemYCGpoBugxJTyDIhakKWCWErkwvfBXo1SKuQVDM/TRVU\nNxOc0Pp/6i74/C5L73bwGuDRwNAz+5hqA93frrTf/94UtMUywaniLNAvQ9LSi3Glnv/pZKa9Bol0\nF02d+2js2Edj515ae47gug4+I0Jx9nJKclZSkr2C/Mh8VEXFdkx+tuXPiCVOn7NOQwtwz8Z/J+wv\nnMQjyZhp53/CpFOoh/aj7tmFWnMC1+PFWboc56qrccsqJywgJ+d/asn5n3qKohAOh6e6GUJMKAlC\nTFOu62Yyo0+SC83wPl32ea46kib80x8imPb5b5IUXO6/MUZW4MyfwbDM9OM0kBh+MDjhKjjumQCG\n4yhDAhzKkMDGmXKG10dfXypTvn+bgfocVxkMePSlFd6uHZ69UnUh0h+QyE9BxIKQBQOfY/tUaPFB\now9avHCumImmung0F48OhuZiDHk8bLmeeW5oEAl6cawEhgYe3e3fLvPYo7kYOoM9O2Ds1/1izv/l\nMFV/A3XNfbx6xMvhZmMwmOU3HFaWm2yYn8QzRn+1yz07w5V2/ifqGnQp5afiNbic59+0E7T2vENz\n9wGaew7QGjuM7aTRVR8FkSUYmp+a9tfHrGd5+Ye5uurPLmu7p+v5h+n9N6B2deI9tA/Pwb1oPd3Y\nWTmDwzUCxSVyDZrCfcp7wNTuc6KuQWfnKhBitpHhGGJW8hmwrCTN23Xnn2piXoE1LABxsRQl80+F\n/k/+Z9c59j5CIYjHzTHLuS6caDXoTpzptuAo0OXJ/MtNQ/SsmUcDDlT2QVVfpiWWAikVSu+yiVtJ\nTFshbdH/U8G0IW0rmJZC2s4sj6dU0n2Zx0OXZz44B8/bZl0dCFyA11DQ1WAmUKGdWZ4JeLjktJh4\nDUj0GoNBDM8oAZGzgxszUUsP/GRbkER6eBeUhKmy/YSX2g6Nj63tHTMQIcR0Ymh+SrJXUZK9CgDb\nMWmPH8sEJbr3U9vxxrjqOdGyecwghJgcTlY2ifWbSKzbiF5fg/fAXvxvvIZ/6yu4cxfgWVRNev5C\n0GW4hsjk5erqUzF0KI7aM/69Wghx+cmtrZi1blycpKFLpyU2er/2qN/hvcsSk9yqS6cosKoixSuH\n/aOub/VC5SiHpQz5abhg2NDzlEqg2ENelUmo0rqoGTP8gRCd3fH+4EV/cMJSMkGM/uDGQEDDtMFV\nPfQlHNI2pC2FeFId3Ma0Faxak5QFtjP2dIBDgxsDQYyBx0N7a3iGPDaG9NDw9G9jaOBoYFpgaJMX\n3PiPN1US6XPvrLFLZ+sxLzctTk1Og4SYAJpqUBBZQkFkCcvLP8Qzbz9EW/zwmNulrTGS24jJpyhY\n5VVY5VX03nQrnqOHCLxzgNDvn8TxekkvXEqqeiV2UckVlz9FQH2HxpajPk61n/l4EfY5XF2Z4tq5\naQlGCCEGSRBCzFo+A/50XZytx3zsqTdImplvmz2aS3VJmusXpgh5Z+ZopDVVaY63GNR1jvwTrvfD\n8h4X7xh5KvLXJfB6vbQfVmje6qf5NfAX2YQqTcJVJkZ4fOdGUzPn2me4jK/Hh0E8fu7gz0BX0PqG\npmHBiaE9NM7urTFi+VnBjUxAJLPccc93XjJTDurqmeDEsN4ag8GMkb0yzh6yMtr2Q+/JG7s06jvG\nviPbU+9h44LUlOWIEOJyC3hzYBy9l/2enIlvjLh4Xi/pZVfhWXc9fXW1eA/uxXNwL759b2Pn5JFa\nuoLUkmW4IRnbfiU41qLzxK7AiBxZsaTKK4f9nO7W+MCqhMSmhBCABCHELOc14F1LkmxcmKQtruL3\nB/Crcbwz/Ddf1+Cja3t59YiPvXUektaZd/WyXIvofJPU6z7cc+TEiMxPk12dJhz2EFzQi5VQ6K3V\niZ0yaHvLR+sbfry5ZwISnmxn0m8cNBX8KvjHGdwYL9theHCif2iJZvjpiadGHYoyNLgRM9UzQ1eG\nlDt/cCNjILjh0cAc56ywibRKa0yjOGvyppEVYiLNK7iJ2vZtY5Yrzb5mElojLgcnO4fEhhtJrN+E\nXncqM1xj26v4t76MWTWX1NKVmHMXgD7D33zFqEwbnt7jP2+S7ndOe9jfYLG8bOxhp0KI2U/eDcQV\nwdCgOOr0512Y6tZcHoYG716SZNPCJPUdOpYDOUGH3JADQCLHpn2Xl956Hfo/IBsRm+ylabKq08OC\nCrrfJbrIJLrIxElDvN4gfkqnc5+X9l0+jIhNqNIiPMfEl2/P6G8yzhXcGG9OjnOxHUbvrdHfk2Po\n47QNNe06venxTUdiz8wOO0KMqjz3WrKDVXT2njpnGQWVI02/I+TLZ0nJHaiKdAWaEVQVq3IuVuVc\n+pJJPEcO4j24l/B/PoHj85NeXI1yzToIRWS4xixysPFMb9Pz2VnjkSCEEAKQIIQQM56hwZx8a8Ry\nf75N2a19WH0KZkxF0V28OWP3aFA9EJlrEplr4tgJ+hp14qcMeo4adO7zogUcwpWZHBKBkpH7vVJp\nKvg9LplMHWNHDY422zy+c+xLsKa65AadS26fENOFqmjcUv23vHTof9MeOz5ivd+Tw42L/yen2l7l\nrRM/pKbtda5f+CARf+kUtFZcLNfnI7ViNakVq1Hb2/Ae3Iv30D7U3TuI5BWQHhiuETh/YmMx/dW0\nj+/jRFO3jmln7luEEFc2CUIIMcvpARc9cHFd+VUNQuUWoXILdwMkWjTipwxipwy6DnlRPS5Z81x8\npTrBMgtVrijjNjAzS1ff+aNCS4pN/B7pCiFml4A3l49e930O1rzIsZaX6Et14NWDVOVvYm7BjRia\nj8LoEirzNrD1yLd5atfnWF11L0tL7kBRxteDSEwfTm4eiY3vIrHhRsItTTg73sC/9WX8r/0Bs2oe\nqeqVmHPmgyafTmci9wLeohx5OxNC0D+joBBi8p3sa2RLxx62d+0nbk3/WToUFQJFNgXrksz9aIzK\nD8TIrk7R16LQ+GKQYz+N0PBCgO6jBrZM5jAmVYH3r3JQlHPfkSm4XF0pJ1PMTqqqU5V/PTdX/w13\nrv5Hbl3xdRYVvxdD8w2WKYou487V/8TCovfw1ol/5fd7v0RPonEKWy0uiarizl9E7/s/SNefP0jf\nDbeg9sYJP/04Wf/6HQKvPI/TWD/VrRQXyG+Mr7deVsCe8Tm5xOxSVVVFMBgkEolQVlbGgw8+iNsf\nVbvpppv4+c9/Plj20UcfpaioiN27dw+r4xvf+AaqqvL6668PLvvEJz6Bz+cjEokQDodZvnz55BzQ\nDCKXAiEm2f7YCX7c8J8c7q0dXOZVDW4pvJaPF95KYMgN+HSlKODLc/Dlpai6waCjoY/YKZ14jcHp\nzQFQXALFFqEqi3CliR6Urz5Gs6QU7l7dxwsH/XQnhseEC8MWPSmVZ/cHuOfaOD5jihopxBQzNB/X\nzvsLKnOvY+vRf+SpXQ9wddW9LCm5Y6qbJi6B6/OTuuoaUlddg9bWgufgXryH9mO+/RZKSRnehUtJ\nL67G9Y89XbOYfK4LNe0a244PTMnpcmYy8NGtqkhPStvE9PP0/7sex5nYfCCqanDHva9d0DaKovDi\niy+yfv16Tpw4waZNm1i+fDmf+tSnhpV79NFHeeihh3juuedYuXLl4PLGxkZ++ctfUlJSMqLuv/7r\nv+av/uqvLu5grgAShBBiEu3qOcxXjv07ljt8eETKMXmm6TXe6T7F1xfej1/zTlELL44n6pC7Mk3u\nyjRmr0K8JpPYsmWbj5bX/fgKrP6ZNiwITXVrp5cFhRbzC2Icb9Vp6dHQVJfKXIuiqENLj8qj20M8\nvjPIR9f0yjhacUUrylrOnav/iZ0nf8ybJ/6VmrbXueWq/4lO1lQ3TVwiO6+AxKabSWy4iYKeTpwd\n2wm8+iKBV1/EnLuA1NIVmeEaqnTgnWquC0ebdV4/7qWpW6cwYvOBVX109ym8fNh/zu0KIzarJQhx\nxXIcc8KDEBdroOfD3Llz2bBhw4ieDucKQAA89NBD/O3f/i2f//znz1mvGJ1czYWYJLZr851Tvx4R\ngBjqWF89v2nePImtuvyMoEv20jTlt/cx/097KLqhD93v0r7Lx8lfhznwU5W2nV6SbeoFjSOdzRQF\n5hdYXDc/xbVz0xRFM11bCyIOH76ml6YujafeDuBIfkpxhTM0H+vm38+ty79Ob7qNX772SQ42/BbX\nlT+OWUHT0JYux7j303R9+nP0bXw3ancn4d/+mqx//Q7+V19Ea2ud6lZekWwH9jUY/HBLiP/YFURX\n4SNrevnEhjhLik3WzUtza3WCgGf436KiuCwuMvnYtXE88tWnmMaOHDnCli1bmD9//uCyxx577JwB\niFdeeYX29nbuuuuuUev79re/TX5+Ptdffz2vvvrqhLZ9JpLLgRCT5M2ug7Sb3WOWe67tDT5a/G60\nWTAlneaF6AKT6AITx4Leep1kg5/OA17a3/ZhhBxCVSahKhN/gY3kmxupLMfmg6v7eHxngN/t8/O+\nFQmZ2U5c8YqzVnDX6n9ib/2jvHniB9S0vc6GhQ8S8RdPddPEZeIGgqRWryW1ei1ay2m8B/biPbAX\n/843sAqLSVWvIL2oGtd37m/fxaUzbdhb7+GNE166Eyrz8k1uW5agLGfkFyqrK9OsLE9ztFlnT72H\n2nadP78hRtQv3ziI6eu2227DcRx6e3u5++67+cxnPjO47uWXX+a6665jxYoVw7axbZu//Mu/5NFH\nHx21zs9//vN8+9vfJhgM8thjj3HnnXeyb98+ysvLJ/RYZhK55RdikhzsPTWuch1mD82pzoltzBRQ\ndQhXWcy51WX+n/ZQ9t5eguUmPScM6p4JcfznYU5v8dN+1Max5IZlqHkFFu9fmWBfg8FLh3zSg0QI\nwND83FD9+UyviFQrv931WQ41PC29ImYhu6CIvpveQ9efP0js/XfjBEMEXn6erB/8I8H/fALj1HHG\n01VM7WzHu+tNfG9uxTj6DtgXN3PUlSBpwrbjXv755TAvHPBRkmXxX66P8ZE1faMGIAZoKiwutlha\nbGI5CgGZ3UlMc88++yyxWIynnnqKHTt2EI/HB9d94xvfoLW1lfvuu2/YNt/73vfYuHEjS5YsGbXO\nlStXEo1G0XWde+65h/Xr1/P8889P5GHMONITQohp6POHvk2xN49CbzZF3lwKPTkUeXMo9OZQ4MnG\no87sLIWKCsEyi2CZRcF1SZKtGrGTBvEanX2HU2geCJT5CVVZhMpMVM9Ut3jqVZeYJNMKzx/0E/C4\nXDdfZs0QAvp7RVydyRXxxonvc6p9K9cv+Dxhf9FUN01cbpqGuWAx5oLFKL1xvO/sx3NgL+Hf/BIn\nGCK1dAWppStwcnKHbabEYwSffwaj5sSw1IlOIEjf9TeRrh7ezfpKFku4bD5gsvlABMuB5aVprp2b\nJid4YcG9kDcTfOhNKWQFJBAhpq+B3A133HEHTz31FF/96lf51re+BUA0GuW5555j48aNPPDAA3z3\nu98FMj0ktmzZwmOPPQZAa2srd911F9/4xjf45Cc/OWIfqqpKjoizSBBCiEmyMFAxrnIB1cfdRTfS\nmu7idKqd7V0HaEl1YJO5AVBQyDEimaCEJxOYGHhc5M0l2wijzqBxDYoC/gIbf4FN/lqI6IW0vWPT\ntN+m6Q8eFM0lUGIRrjIJVlroviv3In51VZqEqbD5iA+/x5VM40L0MzQ/6+b/VyrzrmPrke/w1K7/\nxtVz7mNx8ftQZtD1UIyfGwyRvHodydXXojU34T24F+/eXfjfeh2zuJT00hWkFy0FxyHy65+idY3s\nYaj29RJ6/hl6bRuu2zQFRzF9dCcU3jjhZW99ZsjfVeVp1s5JEb7I99yQL3PPEk+pZAWkx8mVTp2E\nL88uxz6+8IUvsHbtWh555JHBZbm5ubzwwgts3LiRSCTC1772NX7yk5+QTCYHy1xzzTX8y7/8C+9+\n97sBeOKJJ3jve9+L1+vl8ccf57XXXuN73/veJbdvNpEghBCTZH32MrLrwnRasfOWe1/BdXyk+N3D\nltmuQ7vZTXOqg+ZUB6dT7TSnO2hMtfF2z5FhdRqKToEne0RwYuB5UJv48bOHe2t5pmUrO7oPYboW\npd4C3pt/LTflXn3eyTEUBUIFKqECFe+CNtIxhfgpIzP15xY/vAb+Qptwfx4JI3TlBSQ2zE/Rl1Z4\ndr8Pn+GwZsFUt0iI6aM4ayV3rf4uO079mDeOfz+TK2LBg9IrYjZTFOyiEvqKSujbdDOe40fwHNxL\n4A/PEnjlBexo1qgBiKH8W/6Adc26SWrw9NIWV9l+3MuBRgOP7nLzCoNNS3V6OsfOYXU+wf6eEPGk\nJDESXPDUmZNFOSvJ1uLFi7nhhhv4zne+M2xdaWkpL7zwAps2bSIrK4svfvGLRCKRwfW6rpOdnY3P\n5wPgH/7hHwZ7RCxevJinnnqKqqqqiT+gGUSCEEJMEl3R+Gzlh/j68Z8M9mo4W5W/iLuLbhyxXFNU\nCjzZFHiyWR6eN2J90knTkuocDE6c7g9WHIyf5OXULhLOma77Ic0/OMSjPFxEjhLKBCg8OeR7sjHU\nS7ssPHH6FX7c8DtczgQITiQa+OfaJ/h96za+ddV/Rx9jLvEBnrBLzvI0OcvTWH0K8Vqd+CmDljd9\ntGz3482zCFdahKpMPFnOFZGwUVHglqVJEqbCb3cHyAo7FMu0p0IMMvQA6+d/hqq8Dbx25B95atdn\n+3tF3D7VTRMTTddJL1pKetFSlHgP3gP78G8be8YpNZ1C3b8bFi6dhEZOD01dGtuOezncrBP2udy0\nOMlV5WkqyzNvKD2XWL/fcNFUl3hKeiKJ6evEiRMjlj399NOjlp03bx4NDQ3jqmfLli2X3rhZToIQ\nQkyitVlL+ZsFn+Tf65/hZKJpcLmuaNyUfzX/pfh9F9VTwad6qPAXUuEvHLHOdV16rN5hwYnT6Xaa\nUx1sbt1Jc7ITZ8hQj1wjelYvioEhH7lk6+ERUeOhdnQf4t8b/vOc608mmvjqoX/jy/NGjpcbix5w\nyVpskrXYxE5Db51B/JRO+x4vbTt9GNH+HhKVFr58e1YHJBQF3r8iQcpUeHSrzsfWapRmS3fXK03K\nguZuDceF/LAz+M2jyCjOWskHVv8TO07+O28c//+oadvKe676K1TCU900MQncUIT0kmUEXn9lXOWV\n5qZZH4RwXTjRAi/tC3Cq3SA7YHPb8gTLSkz0yzwhl6Jk8kLEU7P4zVgIcdEkCCHEJFsVWciqpX/J\n4d5a6hLNeFSDFeH5lGUXDcvIe7koikLUCBE1QiwMDs9LEQqF6I5105ruHhacaE51UJ9sYUf3Ibqt\n3sHyHkWncNgQj2yqkqVEnQCF3hx+0zz2PMg7u97hZF8jcwIlF31Mmgci80wi80wcK0FfY6aHRPc7\nHjr2+NAD/VN/VpoEimfn1J+aCn+0uo9f74zw2I4Af7qul/ywzApwJUiY8OphH/sbPaStzA2+qrgs\nKjK5YVGSbEkCN8jQA6xf8N+ozNvA1qPf4RevfYKrqz7BouLbJFfEFcBVx/8aKy2n0dpasHPzmW1R\nbNeFoy062457aezSKAjDXVf1sbjYRJ3AQw16HeJJ+TsTQowkQQghpsiiYAWLguNLVjmRNEWjqL/n\nw0rmj1ifsFM0pwdyUXT096hoZ2/sGKfbOkjXmxe8zy2dey4pCDGUqkOowiJUYeE6kDitEasxiJ8y\n6DroRfU6mfVVJsFSi0scbTKtGBp8/HqHH7wEv3wzyMfXxyUL+SyXMOFn20K0xYd/bem4CoeaPNS0\n63x8fe8FZ7Kf7Uqyr+IDq/+J3fU/Zfvxf+FU21Y2LPwcYZ/kipjN3FAYOycXraN9zLJKfS3Rn/4r\nTiCIWTGn/18Vbjgy5rbTlePAwSaDbce9tMU1yrMt7t1oUxKKT0qcRXpCCCHOZRbdjgshJoJf81Ll\nL6bKXzxineu6mF6XEx21vNNby4/qRx9Hd7ZeO3G5mwlkpv4MlNgESmwK1iVJtWnETunEawx6jnpQ\ndJdgWf9MG+UmmndCmjGp/B746Jpefro92B+I6JVu+bPY5sO+EQGIofrSKr/f5+dP1vWes8yVytAD\n3LTsIUqj17L16D/y1M7Pcs2c/8Ki4vdKr4hZLLniaoKvPH/eMnY4gv1f/5LksSMYtScxak/ieWc/\nCmDn5A4GJKyySlyvb3IafglMG/bWe3jjhJfuhMq8fJP3LktQnmMTCoWYgE6Xowp5Heo65aOGEGIk\nuTIIIS6aoijkeMJ4QlVU+Iv4ScPvsNyxcxPs7D7Mk82bWRNdSqkvf4LaBr58G1++Tf6aFOkutb+H\nhE7TKwFQXQLFFuEqi1ClyXmn7ZjmQj6XP17by0+3hfjVW0HuuTaOb+JnwxKTLGXCgQbPmOVqO3Ra\nY6oMzzmHkuyruGv199hx8t/YfvyfqWl7jesWPkjYNzKnjpj5Uiuvxqg5gefksVHXu4ZB720fwOf1\nYlXOwaqcQwJQEn3odTWZoMTJ4/h278Dtn4nDrKjCrJiDVVQK+vS5lU6ZsKvWw1snvfSlFRYXm9x9\ndYrCyNRcC0I+6QkhhBjd9LlyCiFmtIDmY33WMrZ07jlvOQWFQm82/6/hWX5U/wyl3nzWZC1hbXQp\nS0JVE9Y+T5ZDblaK3JUpzN4zU382v+6jeauPYDEEyj2Eqiw8U3TDdimyAy5/vKaXR7eHeHxnkI+u\n6cW4zInGxNRq6tZI2+O7oa9t18kPpye4RTOXRw9w3YLPUtWfK+Kpnf9tsFeEmGVUlfgdH8L31uv4\n9u5C7c10A3AVBXPOfBLrN2EXjByW4/oDmAuXYC5ckqmmuwuj9iR67Um8e3bhf2Mrrm5glpVj9Q/f\nsPMKpiSfRF9K4a1THnbWeDFtWFFmcu3c1JQPywp5HRJpFdvJ5DESQogBEoQQQlw2dxfdxBtdB0i7\n1jnL3F68gftL7iJpp9kTO8qb3QfZ3P42Tza/SlDzc/3pq9hYuIp5TiEh6PGy1AAAIABJREFUPTAh\n7TSCLtnVabKr09jJzNSfiXo/bTt9tL6p4M2xCVWahKpMvDljT/3p2hA7aRA7ZeCkQQ+5RBemCRRN\n7owVBRGHD1/Tyy/eDPLk2wHuXt3HBeRlE9OM7UBHr0pbXKM1plLTPv63bFtG5IxLSfaq/l4RP8r0\nimjfyh9Fv0Y0OHL4mZjBNI3kuo0k125AazmNYlnYWdm4ofHPlOJEs0gtX0Vq+SpwXbTW5v6hG6fw\nb91M4NWXcPyBM70kKubgRKITeFDQnVB45ajCW8fDKAqsqkizdk6KsG96XABC/UMDe1MKEf/0aJMQ\nYnqQIIQQ4rKZFyjlr+b9GX934mcknNSI9Zuyr+LB+R8l1ZfEp3m4Nquaa7OqcSocjvc18Gb3Qd6O\nH+W5xm2oqCwNVbEmuoS1WUsp8xVMSJs1n0t0oUnpai89nXF663Vipww693tpf9uHEbYJ9Q/Z8BeO\nnPoz1aFS/3wQKz78037PEQ+BUpPSd/ehjt2D/rIpy7H54Oo+Ht8Z4Hf7/LxvRWK2JXqfdRwHOvtU\nWmMqrXGN+KEUpzsdWrojOG7mxQt6HLKDDuACY7+g245nxoIvLjIpzbYnNAP+TJfpFfEAlXkbeP3/\nZ++94+uo7rz/98ydub2p92JJtpoL7jZgnJgeAiEY20ACSTaQ10OA7GY3ZJ9kwxOSTQjZfZJNICG/\n5UkDTC+hN7OAg3EHGxfZlotkdatY7Uq3TPv9cSXZsmVblmVZts/79dLr6s6cOefMuXPnzvmcb9n9\nMH98ZymLpv0Taa75x01JLDgLkWWM9FEIiixJGKnpGKnpRGbNB11HaawbECXs772JZFkYwYSBIJd6\nTh6W8+RTcA9FW0hmzV4H2xtUHCrMK4wyMy+G2z6+JvpeZ9wSIxSV8btEGmmBQHAIybKs8XXHEgDQ\n3d2N+GjODP0PnWL8R05ID/NO0xrWHtxGzNTJcadxbcbFFPvyTnisJEk0R9pZ07aVNW1b+LRjFzFT\nI9uVyvykKcxPnMKUQBGKfHp9DUwDumuhY69Ex14JvVdCcVsECyyCRRa+HDAiUPGkjN577ImKP89i\n4pfH3iT2sxqJ59dKXDjJ4upp1rCFCHH9nz5MEw72wIFOaO6SaO6EA10Srd1gmH1ig8MiNQBpfkj1\nW6QGLNL84O4Lovr0apltdcf/MBM8FsUZFtvrJbrDEl6nRVmWRXm2xYQUYRZ9PKJaiI93/YGK2jfI\nSZrJ56fci98lMmiMJefEPSgcRtq/D7lqD/K+PUgHW7GQsDIysSYUYRZMxMrJBeXkgvfUH4SVO2Uq\n6sDrgosnWcwusHCMYgyg0Rz/UAR+8aqNr1xkUJZ1ytWdN0iShM83fCsdgeBsRIgQ4xTLsmhsbByz\n9uLRkscoXPIotnmydQynfEZG3Ax3LMcfxGfQz5HjHzFjbOnaw/rOCjZ07uCg1oXH5mKmv5jZgVJm\nBkrwjYLbxvH6ZZkQbrYR6gtsqXXbkO0Wisck1n5iMST32hCutKNXgU73+H9SbefdChcLJ0W4sCg6\nrDbF9X/qdVgWdPTKtIZkWrpttIbiLhVtIRm9T2xwqiYpXpNkn0Gy1ySl79XjsI77GRzskXl8jYdw\nbGglwSZbLJvdQ16SgWVBfYeNXU0qu5pUOsMyTtVkYqpOcbrGhGQdxTZ6Y3GyjMf7D8S/A/uaVvPa\n2p8QM3qYPeEfmJR+1Wm3ihCfQZxz8R4kd3Wi1FYPWErIvT1YNgUrL59IZi5abn48LsUQ15hlQc1B\nG6v3OqhuVQm6DeYXRJmcpaHYxvf4Wxb88m0/V5RFmJF3/Bg14vo/REZGhrDCOsv4yU9+Qn19PY8+\n+uiZ7spZg3DHEAgE4xqnbGdOsIw5wTJMy2RfbwPrOytY31nB36s3IyNT6s1nTqCU2cEysh0po/7j\nLcngTjdwpxukzIm7YISqVdo+G16Oz85KO66005OW9HjMzI8R1iRWVjpx2S2m54pAhaOJZUF7D+xv\nVmjtc6Vo7RMd+sUGh2KR7DPICBhMyYqRm2rHo/TgsQ/fOuVwEj0mX53Xw5tbXdQfkfouyWtwZXmY\nvKS44CVJkJ1gkJ1gsKgkwoEumV1NKjubVLbW27ErFkUpGsXpGlMnnPJwnFMUpF/I9TN/z4Z9f2LN\nnt+zv3U1F068B+9pcgsTnNuY/gCx8mnEyqfF40m0taDUVOGqr8W17iPcq97HdLrQcvLR+2JKGIEE\n9jQrrN7roKFDIdVn8KULeinJ0M4a9ypJiseFEBkyBOOV/Px8WlpasNls+P1+Fi9ezG9+8xshAo0B\nQoQQCARnDbIkU+TJpsiTzS2ZV9AW62RD5w7Wd1bwZMM7/KX+DTIcyXFBIlBGuW8CijS6bhuSBM4k\nE0dilLZNw8sX312tYMTc2OwWst3C5oj/xfwSmqUMbJMdFja7hTSK5vIXFUXpjUm8vc2JUzWZPXH0\n6h5rLJNRHZtht2tBd0QasGo43LpBMyTAg12xSPYapPkNyrNiA9YNXsdgscHrtRMKnZoBYrLX5Lb5\nPRzokqk5qGBZkOY3BsSHoZAkSA+YpAeiLCyO0tots7PPQqJik53Xt1gUJLuZlK5RlKqJFK+AXfFw\n0aTvDMSKeOXTu5g94ZtMTL9SPKAKRo4kYSSnYiSnol5yKaHOTpTG+kOZN95/l13eqbyfcikH1AC5\nzi6WTtUpyLKdlfF9PA6TUFT4gJ3v/N8XL8YwtdPahk1W+d7iVSd1jCRJvPfee8yfP599+/ZxySWX\nMGXKFG6//fbT1EtBP0KEEAgEZy1J9gBXpczjqpR5g9w2Pmr/jFeaP8JjczLjMLcNv+IZtbYlCWTV\nwtRO/FQoKxZmFPRuGSMmYUQlzJgEVnwCe1TdalyMsDkOiRZHChX97wlC1JAHyshH3NUlCS4vixDR\nJF7d7CboM8nwjtIgjAFaj0THdgedlSpGREZSLHwTNBImR3EmjW6sDcuCUPSQ2NDabaMlJNMWshHV\n45+zaouLDclek9IMjewUO16lB79zZJYNp0Ka3yTNPzLrlmSfycW+KBdPjHKwR6a63cOW/RKvfebG\nJlvkJ8VdNiam6eMu2N1Yk504s88q4o+s3vM7qls/FlYRgtHDZkPPziWSkcuWrMtZt1elI6IwUW7k\n+oNPUXRgM3wGemoaWs4EtLwJ6Jk5oJ4dSqHXYRGKnIXqyVhgmqh7dmHfW4mkaxiBINHJF0DGuZed\nxzC10y5CjJT+yAQFBQVcdNFFbN68GYC//OUvPPjggzQ2NlJQUMBvf/tbFi5cCEBrayu33nora9as\nYebMmZSUlAzU19HRwc0338zGjRuRZZnrr7+e3/3ud6iqysqVK7n99tvZvXv3QHlZlqmrqyMzcxSC\n5p5FCBFCIBCcExzutmFZFnvD9WzoqGB95w5+Xf0MMhKl/dk2AvFsG6e6mumdoNFVeeLUF6lzI/gK\nBqcttSxw2710HezBiEmY0UPihBGVDm3re411yoPeW+bhfT8UwEqyHS1U2BwWc1SLgG6y5m2ZGYUO\nkhKMQeKGzWEhjbNfhEiLjbq33RiHraJZukTXbjtde1UyLgnjLzr5hxrLgp6YxIEeqGmx09odt2po\n7bYR6RMbFNkiuS9mw6Q0bcCyIeAafcuGM02ixyQ3zWJGdg9dYYldB+IWEm9udSFtg7xEg+J0jUlp\nGt5xkvpvrIlbRfyjsIoQjDpRDTbV2Flf7aAnKlGaofHlgm7SA27gGtpDC1BrqlFrqnHs3Ibrk7VY\nNht6ZjZa7gSk4jLwBRiv+Zi9DpPGznH24zIOsDU34X3tBWxdnYO2uz5ZhzZzLvabbjtDPTt/qays\n5KOPPuL73/8+EI/N8cEHH5CRkcGf//xnbrrpJmpqalBVlW9/+9ukpaXR0tLCxo0bueqqq1i2bBkA\npmly9913c+WVV9LW1sY111zDH/7wB77zne8AHPWbcb7+hoi7gkAgOOeQJIkidzZF7mxu7nPb2Ni5\nk/WdFTzVsIK/1r9JhiNpQJAo847MIT6hPErXbrXPomFoVJ+JN18/arskgc0Bqs9C5eQmdpYFlgFG\nVMKpeOhuDw8SKA5/NaISWkjGiEpkRSVSwhD9xEnDUBVLFlWuXhSnhGXzHG2FcZhgcfS+IWOqjRhT\nh7p3BwsQgwtINP7dhSPJwJFwbIuI3qhEy2FWDf2vES1er012kuSJiw2FKTrJPoMUr0nAbZ41ftej\nid9lMTs/xuz8GD1RicoDKjubFFZUOHlnu5N0p0lxmkZ5QYyA6/wTJLITZ/Glw60i2lZz0cR78DhS\nznTXBGcZvVGJNVUSa3f7iRkwJVtjXkGURM/g+5nl9RMrm0qsbCpYFvLB1j5RogrXhtVIH39I0OFE\nz8lD64snYQYTR/eGfAp4nRah5vHRl/GC3NWJ76WnkcO9Q+43P1kHQoQYM66++mpM06Snp4fFixfz\n7W9/G4CrrrpqoMw3v/lN7rvvPnbv3k1JSQkvv/wye/fuRVVV5s+fz3XXXTdQNjExkWuuuQaAtLQ0\nvvWtb/Hee+8NiBBHcr7miBAihEAgOOdJsge4MmUuV6bMJWpqbOnew/qOCj5u38qrzatwy07mJJUz\n3TORWSfhtuFMMkm/JEzT311DChGK2yTrip5Rj2MgSSApcTcPlxcMx/Dzr9vsXv7fe6BFJBZP7cUj\nM8gCw2P3o0Usug9G4yJGWCbWeZi4ETvWw2RciLDZ+ywx+gWKI987ht52JF17VYzwCQbOlOiosJN2\nUYTemDTIhaI/QGRvXxYJWbIGxIb8ZJ0Un0FemhO7FRqvC4hnHI/DojyokVKh0N4AjU6JepeNj8I2\nVu53kuYxKM2OB7Y8cuJ0LuNQvFw86Z/IT76I1bt/x8uf3MXsgtuZmHb5ebuiJRg+XWGJdVUONtfY\nkSS4ICfGnAlR/MMR9SQJMymFaFIK0emzwTDwdXWg7apAranC/eEKJNPE8PkHAlxqOflYnjPng+d1\nmPRGJUyL81LYHQrnp+uPKUAIxp63336b+fPn89prr/Gd73yHUChEYmIiL7/8Mv/+7/9OVVUVlmUR\nCoVoa2ujpaUFwzDIzs4eqCMnJ4fW1lYAQqEQd911F++//z5dXV2YpsmcOXPO1OmNW4QIIRAIzisc\nssrsQCmzA6VYlsW+cAPrOyr4JLSLD1s+QUai5DC3jZwTuG0EJmo4Eg06Khx0V6mYGqgeC39xjGBJ\nDGWcrRa77LB0bg9PrPHwwk4Xt87vwXuYCJCREfczbmxsG/J4yyQuWMQYEC4Ot7o4cpveIw96fyyr\nEVm1kO2+AVEi1jG0MqABYQV6bRCyQU+jnZ43VXo0GVMCJAufxyTRZ5CbExcbkv0mCW4T2xFVer0w\nxhnhziq0boma17wYYRk7kBeO/2kSNDmgISqzKuzgw11OUnxxl43iNI0UnzleFmFPK9mJsw9ZRex+\niOrWVcIqQnBM2kIya/c52FavYlcs5hVEWViuYmqRkVdqs2Hl5BFJSCIybwHEYqj1NX1BLqtxbN8C\ngJ6cipY7AT03H4pLR+eEhonXYWEh0RuVzlt3rkGYJvaKLWe6F4LD6LdEuPbaa3nllVf42c9+xoMP\nPsgtt9zCyy+/zBVXXAFAZmYmlmWRkpKCoijU1taSm5sLQG1tLS6XC4Bf//rXtLa2smXLFhISEnj0\n0Ud55plnAPB4PITDh7KlHThw4LwVr4UIIRAIzlskSaLQnUWhO4s7vF+mpr2BDZ072NCxg2caV/BY\n/Zuk2xOZHSxjTqCUcm8B6pGRH+mziFgQJn3B2KfhHAleh8VNc3p4Yo2XZzd4uGVuaNjZECQZbE4L\nmxMYiRuJxiHB4jDrCgUn4a7YwL5Iu4wmxVuQAJsFMqACqg7+fg+XXgk6Dv8BlwBb31+cNtnioA1k\n2UKyEf+TLWyqjCV5Bt4P7LPFM5RItr5tMsj9/w/si/8fc0tENeXQvr7y0mHl5cPKW0Z8HM6GZ47m\nda4hrVFUC3IikBORUFJ19DlRdjWpbKhysGq3kwS3QUm6RnG6TnrAGHfnGuuS4oJhTEJxW/gKNZQR\nTo4GW0U8LKwiBEfR1CmzZq+TnU0KXofF54ojXJAbw6GA26ESOkZYG8PUiOrdKLITu+IeXmN2O9qE\nIrQJRQBIPSHU2rjrhr2yAten67BkG76MLLTcfPTcCejpmac1nkS/yB0SIkScaAQ5egrC01mKTT79\ngVRHo43vfe97zJkzh+9+97tomkZKSgqmafLwww8PWDr0B5u8//77+cMf/sCmTZt47bXXWLp0KQDd\n3d243W58Ph/79+/nkUceITExEYBJkybR3t7ORx99xNy5c/nZz352yn0+WxEihEAgEPSRqPq5Mnku\nVybH3Ta29rltrGnfymvNq3DJDmb4i5kTLGVmoJTAKGbbGGsS3BY3ze7hybVeXvjEw7LZPag2iGoW\nDe0mbe02kr0GjlF8bpAkkOwg2y1Ub/xhNKZDa8hGSINaQ6LVkmmN2OhK6nsotsBjgF+DgBYXH3wa\neI24zKB4DbIu68UypPgE35AwTQ57D5Z5aF/8ffx/RVaJRcy+933H6mBF5UPlD9s3uC6ICx4wVIaT\n4+MfJIYcKWA0uiLINogZ7j4BY2hx43AxZCghRT5e+UH7ju6h3iMR2n/iRwS9WSFPDVN8QRjdCLO/\nTWFXk8rmWjtr9jkJuEwmpcVdNrITzqwgYcRg7+syHXt8HPrsoGW9k2BpjJQ5kRG7TmUnzuZLMx5h\n/b7/x+rdD7G/L4OGx5E8Op0XnFVYFtQetLF6r4OqVpWg2+CqyWGmZGkoJ8ga3RVuYFvdS+xrWYlu\nhAGJzOAFlGV9iezEWSfXD4+XWMlkYiWT4/EkOg7iPdCAtXsXzk/XIa/5O5bdjpadF3fdyJ2AlZ4+\nqgKa1xl31eqOyKQHzh+3rWOi6wPi+vnEyabOHCuOvNZLSkpYuHAhf/zjH/nP//xPrrjiCmRZ5s47\n76SoqGig3MMPP8zXvvY1UlNTmTVrFl/96leJxeLZqv7xH/+RZcuWkZiYSElJCTfccAMffvghAH6/\nn9/+9rcsWbIERVH4xS9+wSOPPDJm5zuekKzzNRrGOMeyLBobG8esPa/XS2iM7ZJHo82TrWM45TP6\nUiON5fiD+Az6GY/jb1kWVeEG1ndWsKFjB5W9tchIFHvymB2Mu23kOtNO+sFtPIx/XbuNp9d5yE3U\nCbpNKhodRPpW5lSbRXmmxoJJkUEuGyNBMxiI2dAastHSl5Gi87CV9qArHrMh2Wvg10Hf6MSnH27T\ncDQpc8IkTh1ZmspTGX/LAixwO72EunqwDI4QP4YWMOyKk0hvdJAYYhlgHlbe5XBj6tDbEzmOkHJI\nDDH7/sc8hcdaebAwgQlGZHgzcl9BDE+OHo/1ocbjfKBAY49EZZtKZYtKKCrjccQFiZJ0jdxEA79/\n7K5/04DaNzxEmo8trPgnxci4JHzK96DagxtYs/thNCPCnILbKRqmVYT4DYgzHn8DmrtkKhpVIpqE\n12ExOStG0H30PdGyYE+zwpq9Duo7FFJ9BvMLo5Ska0MaGxzZZkv3LlZs+z/E9J4h+zEj/2tMzVky\n7H4f9zxNE9uBRtSaKtSaapTGOiTDAH8AeWIxXcnpaLn5WF7fiSs9DqYJv3zbz9WTw1yQe+xsRuf0\n9a9p2Pftxr5zG2r1PjCNE4oQzv/8/Sn1SyAY7whLCIFAIDgBkiRR4M6iwJ3FTRmX0651saFzJ+s7\nKni28T0er3+LNHtiPI5EsIzJx3DbGI9kJxhcMzXMK5tdHLk2oxkSm2vtVLUq3Do/hG8YprS6AW09\nMi19gSH7A0V29MoD9fudcbGhJF0byEaRl+4iFhn8IFdbo9DbcOxxVLwmgeKRCRCniiQBEtjscfeU\nQxx/jLxeB6HQ8fuckZEAQGPjwZPqU3/WFGsYYsiR5eyKk3A4OlAu2i4Tqjpx+lmA7iqV7n1Dl80H\nJsgWlmKhyxK99XbqcFBjs/B4wedxE/AZKI64hYysWoPEDFnt2263kE+wgnzcPu5VjytAAHRV2kko\nj0LGyNsByEmcTWqfVcTHux+iWlhFnLWEYxKvbHZR1TrYJGzVbgflWRpXTw6j2OIT7R2NKmv2OWjp\ntpGdoLNkVg+FKfqwrX8MU+P9ip8fU4AA+LT6MVJ9JaQHp5zKacWRZYyMLIyMLCJzLwYthlpfS7Ct\nBXPPLryfrAdAT0xGz50Qz7yRnQcOx8k2g8duETpWpqNzFcNA3b8P+64K7Ht3IWkaenomxmVXEbap\n+N56+Uz3UCA4o5wdT8kCgUAwjkhQ/VyRPIcrkucQMzW2dO9lfWcFazu283rLxwNuG7ODpczylxBQ\nz1xk8uGwr0XheMahnWGZd7a7uHHmoWjeugEHe+LWDAOpL7vjYoPVV5fPaZLsNZiUppPsNUj2xd87\nhvjlsStw5NQ887IeGv7HTW/90T4h9oBB1hW92E7ueficpj9rShzriNfjc6Q4ovdKhParw7KuyPty\nCLvfxNTi8T1MrS9o6cD7vtdYPB5Ib69Md0gmEpJpb1cJoeAAFIPjpruVZGtAqJDt8WCmdreMKbni\nwsXhokWfiGHrez24bXiCSscOO0weVtHj4lC9LCj+bjxWxJ54Bo24VcRlIlbEWYJuwDMb3DR1Hn3D\nspDYVm8nokkUpeqs3Weno9dGQYrGleVhchKHn7Gon+rWVYRjJxYedzS8NjoixJGodrT8QpT5FwPQ\ntHcPau1+lJoq1H2VODdvwJIk9PSsgcwbekYW2E6sDnqdFqHoeXDdWxZKfQ32ndux796JHAljJCYT\nnn0hseIyzGAiXq8XLRSit7sT96oPhq4nEBzbfgsEZwAhQggEAsEpYJdVZgVKmBUowcqxqA43xt02\nOnfw2+rnACj25DInUMac4MjcNk4n4ZjEjsYTB37YfUBhRYWT7ohMa7fMwV4Zq2/C6HXExYXCVJ2U\nw8SG4Qa7PBY2O+Rc3Uu42UZnpYreIyPbLfwFGp7c4a8wCk4exW3hy9eOaeHQjytdx5kY9/OWFQtO\nIhuMx+OluqmXnU0qnzaptHTZcNgsChN0ihJ1cnw6ijm0mGFq/dlWbOg9MrGOI7aP0DUl1nEK5hZD\nkJM0h+v9j7B+36N8vPu37G/9mPkT7xZWEWcBFQ3qkALE4expVtnTrFCaofHl6b0jjnlgWRb7W1YP\nq2ztwfUjauNksdweYsVlxIrL4vEkOjv6XDeqcGzeiGvdKixVRcvO7cu8MQEjKWXIqLseh3nuWkJY\nFrYDjdh3bce+qwJbqBvD5yc6+QJiJeUYyalDjklk9oXo6Zk4N21A3bcbybIw3R6iUy4gcMU1Z+BE\nBIKxRYgQAoFAMEpIksQEdyYT3Jksy7iMdq2bjZ072NC5g+ea/ofHG94i1Z7AnEAZs4OlzHNPPdNd\npq7dhj6sCZvEljo7GQGD/GSdWV4znv7Sa+Kyn97QQq5UA1fqya8sCk6NlLkRwgcU9J6hJw+ywyTt\nopFnhJEkSPGZpPiiLJgY5WCPzK6meGDLV3e7sMkW+Uk6JekaRXk67iGuM6/XRig02Hy93y3lcMuM\nmjc8WNqJJ0GmBpZpIcmjp3DFrSL+uc8q4vdxq4jCOyhKvXRcCZKCwWyqHZ71TFGqxvXTD30PdDNG\nVOsiqncT1boPe+0iqoX6XuPbNSNEOBYva1nDu8eZlo5pGchDRZQ9XUgSZjCBaDCB6NQZ8XgSLQcG\n4km4V32AZLyH6fbE3Tb6RAnT5wfiGTJaus8tEUJub8O+cztq5Q7sB1sxXW5ik0rpKS5Hz8weVgok\nPSefUE5+/KZl6KDElftg37gJBOcyQoQQCASC00SC6uPy5Dlc3ue2sfVIt419Di7wTWROoIxZgVKC\nZ8BtwzwJ/eCy0jDTco4dWExwbqF6LHKvC9G81kWoWjnkKiFZeLJ0UuZFcARHL9p9osdkfmGM+YUx\nOsMSu5pUdjWpvLHVhbQN8hJ1itN1JqVrxw2U2u+WcrhlhjdHP6FVB0C0TWH1b8KklCjIqTbcGcaI\nM2YcSU7SXK73l8WtIip/E7eKKLpLWEWMEywLemMS3RGJ7ohMc5cERJDQibs1yVg4AQtV3oFD+RhF\nbqAn3MErn3YQ00NEtS50MzpE7RIOxYtD9eFQfDhUPz5nOl53IrLlxKH4qT24nvr2jSfsp1NNGFsB\nYihkGSMtAyMtg8jsC0HXUBrqUGv60oHu3I4EGAlJaLn5BNwXUxVJP7N9HgWk7i4clRXYd25HaW7C\nstsxS8rpXXgZWu6Ekac6laQBAUIgOF8QIoRAIBCMAXZZZWaghJmBEv5Xn9vGZ+G9rGrZzEP7nwfi\nbhv9wS3znKObJu1YpPhMGGbCsHhZwfmE6rHIurQXvUci3Byf+DiSDOz+02v9EnBZzJkQY86EGKGo\nRGWTyq4mhXcrnLyz3UlOgkFxusb0guE9yATLYicWIWSLrMt7kToDtO40iHziRXaY+PJ0vBM0PJn6\nkOlMTwaH6mNB8b+Ql3wxa3b/jlc+vYs5Bd+iMHXRqVUsOCaWZRHTwrT1hunojdHRa9AVNumO2OiJ\nqfTGnEQ1D1HDxLIOX4E2kKUubFILNrkFWWpBkRtwKB/11evHtAJIUpBUf8aAuOBUfDhUH3bFh1P1\n4VD82BUP0hBq1uGZFdIDk4clQkS0dt7d9n+YlrOUtMAoBDAZDRQVvc/6IcznkSJhlD5BQq3eR7Jk\npyf1OrxP/wWjLxWonpEFyvifhkjhXuy7d2LftR2lrgZsNrQJRXTPvhCtoAhvMAFtjLN6CATnAuP/\n2y8QCATnGP1uG1NSJ3F90gLatW4+6dzJ+s4Knm96nyca3ibVnhAXJAJlTPEVnrZsG4kek/wkneq2\n46/CpPt1MoPCJeJ8RfFY+CboZ6Rtr8NiRl6MGXkxemMSew4o7GxS+WCXk/d2SGQEPBSnaxSn6yR6\nhhbK3OkGSTMitH3qHLoRySLz8714c3Qy5tgpvMyieltzPPNHtUrs5vlIAAAgAElEQVRnpR3ZbuHN\n1fDma3iydU7lK5mbNJc0fxnr9j3Kqsr/orp1FZdN+1fgGP0TAPEMEnH3hi6ifZYHPbFeOntNuiMS\noahCb9ROWHMR0b1oegDNTMS0Uhmc7DeKTW5BlduxKwfwO3vwOKO4lChep4bPKVHT5qKmPRHL8qGb\nuZjWZEzLD9F/HNSnz+eGmVd46ll6gp5citIuY8+B945ZxuNIZWrOUnY0vMZbW/43qf4y5k76GonO\nsnHl2mM5XWiTStEmlQIg74ti7rQR8qcR3LoJ1/qPsRQFLSt3IPMGnoIz2+nDicWw76uMu1vs3weW\nhZ6TT88VX0QrKsZyiO+pQHCqCBFCIBAIzjAJqo/LkmdzWfJsNFNna/deNnTuYH1nBW+0rMYp25nu\nn3Ta3DYWlUZYvkYhZgz9EGuTLS4ri4xqmwLBSHDbLabmaEzN0YhqUNfl47P9Fqt2O/lwl0Sqz+gT\nJDSSveYgt+zkGVH86XYaN5hEWvoef/pcSxIviOJOPySySZKEM9nEmRwleVaUWLs8IEh07bEjKRbe\nHA3fBA1Pjo48Aktqh+rjkuJ/Ib/PKuLpj77O7II7KExdhCRJ6EaUqpaV7Gl+n3CsA4fipSBlIYVp\nl2JX3Kc2kGcYyzKJ6T1HxUmI6N1UtppEYl20dTTRGzPoiar0ak4imgfNCGBYqZhmMoaVjmFOwWKw\n/7xN7sGhhHCqPQRdUTyOBnzOOgIuiYBbJdFjJ+D0oCpeJMk3cNzhVgkAeckyf1nlHcj2MxSqLX49\njhYXFt2NLCnsbnoXi8GCWqKnkM+X/RCfM41J6VdQd3ADn9U+x2sb/5VETyFTc5aQl3zhkBYXZxpP\nQvx6bV7wRfBd3RdPohqlpgrXmpW4P/ofLLcHT3beQOYNc6QZInQdpaEWSdMwAwkYySnDO84wUKv3\nxgNM7t2NpGtoGVn0LryM2MRSLM/4znIlEJxtCBFCIBAIxhGqrDAjUMyMQDHfsr7E/kgT6zvi2Tb6\n3TYmeXKYHShjTqCUyZ6Jp9xmmt/k5rk9vLnVRUv3YHvzRI/BVZNHlnJOIDidOFSYlmdRmNSLZsRT\nze5sUllX5eCj3U4SPYcEiXR/XJBInGRhz+wh1i1hRiUUt4XiPr5riSSBI9HEkRgleWaUWIdMd7VK\nd5VKw/t2JJuFJ1uPCxK5GrbhxTMcoN8q4pOaPw1YRUzNXsZHlb+iO9I4qGxL904+q32WyyffT5K3\n6GSHbNSxLAtNDxOKNA9YJgwKxDgoOGPceiGmdxOJ9WJYQQwrFcNMxrRSMKwULCsLi3QMKxlNT8Di\n8MG0cKoRPPYYXoeB32Xhd5kkuEP4XeBzWvicJqoN4hYlp7ZaneY3ubwswrsVToZyV7PJFl+6oHfI\ngKkjRZYVLpx4N1NylrDnwHv0RFtQbS7yki4clJZTkmRykuaSnTiHzthu1u36Kx/ufJCAK5spOUso\nSFmIfJqs50aC1xkXVLqjMql+EyM1HSM1HWbNi4sGjfV4muqx7dmF/X/eQrIsjEDCoSCXOXlYrhMI\nb4aBa83fcWzdhBw5FChUy8gifNHn0HPyjz7GNFFqquPCw+6dyNEIenIq4bkXESsuH7kQIhAITohk\nWdbpdewUjAjLsmhsbDxxwVHiyBWAs6XNk61jOOUzMjIAxnT8QXwG/YjxPzYdWmgg28amrkrCZpQ0\nRyIz/cUDbhv2EyzJejweVjZ8wtsta9jbW48syUz2FnBN6oVM8uTSK6Wy74BJV1c3mUGdvCRjTFJh\nng3jPxp1jNd70Lk0/roB1W3xLBuVBxQimkzAZVKcrnHBBIVER+i41/TJjH+sSyJUHbeQiDQrSLKF\nOysuSHhzdWzO4T9ieb1etlevYM2e3xPROonHahkapxrk+pm/x6kGhl3/sdrsHz/T1ONiwSAx4egs\nD5E+q4WY3k1E68a0BlsCWJYd00pDlnORpGwgA9NKRTeT0IwEYoafiOYCDq3Y22QTr8PC77JIDToI\neiQkvRuf0xwQFzwOC9tpXOQ/1vVY1Wpj3T4HVa0KICFLFsXpGnMLomSMMCXnidocSR3NXTvZWvs8\ntQfX4XGkMiV7MUXpl6PI9iHLH4vTcf8xTPiPtwNcM6X3mJYj/f2SIhGUuv0DmTds7W1YgJGajtYf\nTyIre3AgR9PE+8pz2Kv3Dlm3JcuEvrgYrXBSX0rNBhw7K3Ds3oEU6sbwB4iVlBMr7kupeRKcrt+A\n8eReIxg5jz32GMuXL2fFihVnuivjjvEjkwoEAoHguARV72C3jdBeNvfu4eOWz3izZQ1O2c4F/kPZ\nNhJU36DjdcvgJxV/ZGXrp4O2v3/wE94/+AlL0hfxr7P+gcJ0G42NQ0V4FwjGP4oNilJ1ilJ1rjKh\n5qCNXU0q2xtU1lfJeB0+JqXFLSRyE40RB7QHsPstEqfGSJwaQwsdEiSa/u4CCdyZOr58HW+edkKL\nC4C85PmEtXbW7nnkuOUiWge7m1YwJefGo/ZZlkXM6BkkFByeFvJwywTN7CEc6ySqdaMZvUO2pdrc\n8UCLNj+KkopEKaqSjs2WgqokYpBIT9RFRHPTE3UQ1QdbUzmUuIjgd8UFhSS/gkOODIgLPqeFS7UG\nhKGMjLh7RWPjqcdZGA0mJBtMSO4lrEFUk3DZLRzj8Ok51V/CpeX3cbCniq21L7Bu73+zueZpJmd/\nmeL0q1HPoAuPTQaXahKKnvjLZjmdaEXFaEXFAMjdXSh9goSjYguujWuwbDb0zJw+USIfpanhmAIE\ngGSaeN56hegFM7FX7sTW2Y7p9mBOnkaoYBJGeuawUmoKBCPhVASl1atXc/HFF/PQQw9x9913D2xf\nuXIlixYtwuPxAJCSksKiRYu47777yM3NHVTHU089xVe/+lVeffVVvvjFLw5sf+yxx/jGN77Bj3/8\nY3784x8PbP/pT3/K/fffz1//+lduu+02YrEY9957Ly+++CLd3d2kp6fzzW9+k+9///sjPi8QIoRA\nIBCclaiywgx/MZdkzuQbaV+gJnKA9Z0VbOio4OH9L2BhMcmdw5xgGXMCZeS7Mvhr3RtHCRCH83zT\n+5TUFPKl3IVjeCYCwenDJvdPIg2uKI9wMOJlc5XOriaVT2scuOwmk1J1itM18pP1U1ppV70WCZNj\nJEyOofdKhPbHXTYOrHZyYLUTV5oRt5DI11A9xxYkatrWDqu9bXUv0hmuG9Ja4ch4AgCypA5ka+hP\nFRn0ZWGznNiVAJaVjGElETMSiOl+IrqHcMxJKGqjtUciFJXRBsWNsfA4LAJuCY9DJ91v4nNq+JzR\nQRYM9iOeNOMrwWdfql+XCi51/BsPJ3omsLDkXqbn3cLWuhf5tPoJttQ+T2nmtZRmXouXMxPbwOuw\nCEVPfjJm+vzEyqcRK58Wt2Joa+kTJapwrV+F++MPsIYxyZO1GM5NG4gVl9Fz6dXoOXl4/X4Mkdli\nTFjw5jfRrNMb3FiVFD76wp9OaxtjzZNPPkliYiLLly8fJEIAFBYWUllZiWVZ7Nu3j5/+9KfMmjWL\nTZs2kZWVNWQdh4sQ/XU888wzg0SIp59+mokTD7n6PvDAA+zcuZPPPvuMpKQk9uzZw9atW0/53IQI\nIRAIBGc5kiSR50onz5XOkvRFdGohNnbtZEPHDl5s+pDlDe+QpAZo17pPWNfje18XIoTgnESWID8F\nkl0RLi2N0NhpY1dT3G3jszo7DsWiKFVjXlSnNHt4uTgtC/Y0K9S2K2BBeiAeh0JxWwRLYwRLY+gR\niZ79Ct1VKs3rnDSvceFM1fHlxwNbqr7BE9tIrGNYbcf0Hjp763CoPnzODJJ9kwYEBqfix654cah+\nFNmPZgToiTkIRW10RyS6IzLtEZn6boWOnvjk0LIOTeRssoXX0W+pYJLmP/R/v7jg7XOPiIsKQ1tR\nCM4cflcWF038Dhfk3sy2ur+xre4lttf9jcl51zEp9Yu47Ylj2h+Pc3iWEMdFkjCSUzGSU4nOmBsP\nJrm/Ct8rzw7r8FhRCT1XXHtqfRCMCM3S0czTnGFpBJdXdXU1d999N2vXrsXhcPCDH/yA66+/nrKy\nMpqbm3E647FlHn/8cZYvX867775LR0cHd955J++99x7BYJB7772Xb33rWwD09vZyxx138Oabb1JY\nWMhVV101qL2VK1fyL//yL+zdu5fp06fzxz/+kYKCoTPD6LrOc889x0MPPcTXv/519uzZQ1HR0bGA\nJEmisLCQxx57jFmzZvHrX/+aX/3qVwC0tLSwYsUKnnjiCb7xjW8QCoXweg8Jkfn5+UQiETZu3Mis\nWbPYuHEjCQkJBIOH4qFs3LiRxYsXk5SUBEBRUdGQ/ThZhAghEAgE5xgB1culSbO4NGkWmqmzLbSP\nl5o+pE3rPOGx+3sa2dFRRVCkChScw0gSZAYNMoMGnyuO0tIts6tJZVeTyp/+J4ZdgYJkN8XpGoWp\n2pDm9zVtNl7f4qYzPPjJ12M3uWpymEnp8QduxWkRKNYIFGsYUQjVqISqVVo/cdKy3oUjOe6yoZQD\nKsOO8+BzZnFp+f+lOyL3/cXFhYZO6dC2qEQ4Nrh/9j73CJ/DJMVnkROMHeYaERcZ3HZLWKefI3gc\nKcwt/BbTcpZR0fAKFbVvsKX6b0xMu4zJOYvxOdPHpB8+h8XBnlEO6mGzoWdmnbhcP6fieyU45zAM\ng2uuuYabbrqJl156iVgsxu7du8nOzmbatGm88cYbLF68GIBnn32Wm266CYC77roLWZapq6ujsrKS\nSy+9lNLSUhYsWMD9999PS0sLdXV11NXVcfnll1NSUgJAbW0tS5Ys4eWXX2b+/Pk88sgj3HTTTaxf\nv37I/r355psoisLNN9/Mn//8Z5YvX879999/3HO69tpreeuttwbeP/3000ybNo1ly5bxox/9iBde\neIGvf/3rA/slSeIrX/kKy5cvZ9asWTz55JPceuutvPrqqwNl5s6dy4MPPogkSSxYsGDgfE4VIUII\nBALBOYwqK0z3T2J/uJHN3buHdUynFhIihOC8QZIg1W+S6o+yYFIUmzuNzdUGGyt1XtnsxiZbTEiO\nu2xMTNNwqVDfbuPZDR508+iZek9M5qVNbm6c2UtR6uCVP5sDAhM1AhM1TA1CtSqhKoW2zQ5aN0rY\nE7zkp/wvDkYfpEftxCQZw0zFsJIPS00Zf9/ck8l/rRgciNZtP2SpkJWgHyUu+BwmjsMOiVsxiPgv\n5wNOe4AZ+bcxt/g2PtnzHBX1r1DZ9A4FqQuZkr2EoCf3xJWcAl6HSc3B0Z92WA4nRiABW2f7Ccvq\nqWMjuAjODtatW0coFOK+++4DwG63M336dACWLl3Ks88+y+LFi2lvb2flypU8+eSTmKbJCy+8wO7d\nu3E4HEyZMoXbb7+dp556igULFvDCCy/w2GOP4fF4KC4u5mtf+xrr1q0D4rEZbrjhBi688EIgLmb8\n5Cc/oaam5qg4DhB3o7jxxhuRJImbbrqJX/7ylycUIdLT02lvP/RdePLJJwfEk2XLlrF8+fJBIgTA\nkiVLmDFjBv/xH//Biy++yKZNmwaJED/84Q8JBoP8+c9/5u677yY7O5uHHnqIa6655uQG/AiECCEQ\nCATnAUHFd+JCfbxdv5oFnikUe/KwjcOc8wLB6SQ1IHPFNJkpqa109EpUNqnsOqDyxhYXsuQiL0mn\no1ceUoDox7Ik3t/ppCj1aH9z3YBQtM9awQndEwxCqRZym4q9RSK4u5Tp1uN0K1DvhHoXdCk6NrkV\nWWrBJrfgVGqYlusj0eMcEBm8DgtleF4kgvMYh+plas5SyjKvo7LpXbbVvcTe5g/JS5rPlJylA9kx\nRhuPs9/tZ5RjQEoS0anTcX/0/nGLWapKrGzKccsIzi/q6urIy8sbct+SJUv40Y9+RDgc5qWXXmLh\nwoUEg0Gam5vRdZ2cnJyBsnl5eWzfvh2IZ5XJzs4e2JeTkzMgQtTU1PD444/z3HPPAfEgwrquU19f\nf5QI0d3dzWuvvcY777wDwA033MBdd93F2rVrmTdv3jHPqbGxkYSEBAAqKyvZuHEjzz8fT+++bNky\nHnzwQRoaGsjMzBw4JikpialTp/KDH/yAadOmDbhd9GOz2bjnnnu455576Onp4Re/+AXLli2jtrZ2\noK2RIEQIgUAgOA+YGyzHLTvpNSPHLedXPaxu/ozXYx8RVLzMDpQxL1jONP9EHCdI/ykQnGsE3RZz\nCmLMKYgRikjsOqCyrV6lvffEs/22kI03tjixyQxymeg9wj1CtcWtFRK8Eq4EDd1u4ursJVpTS2Go\nkJKQl4jazEHvStp8H6J567hs8o9J8duAsy/Ao2B8oNiclGVdR3HG1ext/oCttc/z+uZ/YnvTfC4s\n/QdURleM8DpMDFMioseDfI4mkWkzsVfuQDlw7LSivQsvx3IICz/BIXJycti/f/+Q+9LT05k1axav\nvvoqzz//PLfccgsAycnJqKpKTU3NgIBRU1MzMKnPyMigtraWCRMmAHEXjH6ysrL41re+xW9+85sT\n9u2FF14gEomwdOnSgW2WZbF8+fLjihBvvPEGCxfG43otX74cSZKYN28elmUN1PHUU0/xve99b9Bx\nt9xyC7feeitPPfXUcfvl8Xj4wQ9+wAMPPEBVVZUQIQQCgUBwfFw2B9emXsSzTf9z3HL/e8o3uDRj\nDiv3rGNtxzbWdVSwom09Dlllun8ScwPlzA6WEVA8Y9RzgWB84HVazMyL4XOavPjJ8B6fdjTaSfSY\neB0mmQEdb5qF32nidfa/mjiU+Mpw3DUi3HekDW12GnsbX6d+bwPOtnJSu75AZvvN2Nw6lmTQO0HD\nlWogjJUEp4JNVpmUfgVFaZdS3bKKHU1/48kP7iDVX8bUnKVkJcw8pRSD/Xgd8UlQKCLjUo/O3nJK\nqHa6Fn8FzwfvYK+sQDKMgV2GP0D4os8RK5k8um0KTgpVUkYUOPKk2zgJ5syZg8/n42c/+xn33nvv\nQEyIGTNmAHHLgUceeYRPP/10wHpBlmVuvPFGfvSjH/Hoo4+yd+9e/vSnPw3sv/HGG3nggQeYPn06\nDQ0NPP744xQXx9PN3nLLLVx00UXceOONXHTRRYRCId59992BuBOHs3z5cv75n/95UBrMd999l+9+\n97v89re/BRgQFkzTZN++ffz85z9n//79fPe73wXi7h+/+tWvuPnmmwfq+NOf/sQTTzxxlAhxww03\nkJaWxsUXX3xUXx566CFmzpzJrFmzkGWZhx9+mGAwOHBeI0WIEAKBQHCecEvmFYSI8EbTx0ftk5D4\nRvY1XJEZV9hLvfmUevP5RvYXqY00s65jO+s6tvPQ/ueR9sf3zwuWMzdYToYjeaxPRSA4Y5xMGs8r\nysNMzR6ZtYJqc1GSfTUlfZa9lgnhAyG6q+KpP9u3O7C5THz58bSf7gwhSAhGjizZKEhdyIVTl7Gn\n8SM+/Oz/473t95PoKWRqzhJyk+cjSyP39/E648JDKCqRMnzvwOHjcNBz1XX0XnIpatVeJF3DCCag\n504YZf8PwUgYj6kzbTYbr7/+OnfeeSe//vWvcblc/Nu//duACLF48WLuuecerr76avx+/8BxDz/8\nMHfeeSc5OTkEg0F++tOfcskllwDw4x//mDvuuIPc3FwKCwu57bbbBtwx8vPzeeaZZ7j33nvZtWsX\nHo+HRYsWHSVC1NfXs2rVKv7yl7+Qmpo6sP3mm2/mhz/8IW+99RY+n4+qqir8fj+WZZGSksKiRYvY\nsGEDWVlZrFmzhpaWFm6//XY8nkOLRnfeeSc///nP2bZt26A2HQ4HixYtGnh/uPDodDq55557qKqq\nQpZlJk+ezGuvvTao3pEgWf0yimBcYVkWjY3HNisbbeIrMGObK3k02jzZOoZTvt8fcizHH8Rn0I8Y\n/9Nbh9fr5dMDFbzZsoZ9vfXIksxkbwFfSJlPpjPlhOPfrnWxvnMHazu281nXbjRLJ9eZxtxgOfOC\n5RS5s5FHMBM6n8Z/PN6DxPgf4kTjH9Xhd+/7ienHn9jIksW3P9+Nzzm8x6yTORfLgkizLS5IVKvo\nIRmbw8Sbp+OdoOHJ1BnOfHE8fgbiN+D01jHc8W9oaKCpcwtbap+jseMz/K5spuYsoSBlIbJ8cmuY\nXWGJjfvtrNvnJOA0yUnSmZ4TIzvxkMWCGP9DZGRkjIr1iUAwnhGWEALBGLI7dJA3D+yjorsF04JC\nT5ArUwu4IJB64oMFglFikieXSSOMhJ6g+rkyeS5XJs8lbETZ1FXJuo7tvN2ylueb3idR9TM3UMbc\nYDlTfUWoJ/mwKhCMdxwKTMmK8cl+x3HLTUzThy1AnCySBK40A1eaQcrcCNE2ecBCorPSjmy38OZq\ncUEiS0d8DQUniyRJZASnkRGcRkvXLrbUPseqyv9i0/4nmZK9mKK0y1Bsx/8OAGypU3lrqwvTik+q\nOyMynfV2ttXbKc2Ice208ElZFwkEgnMD8bMkEIwRT9Ru48WGXYO2tcR6WdvewMWJ2dw37fNnqGcC\nwchw2RxcmDCFCxOmYFgGFaFq1nVsZ23Hdt5qXYtLdjAjUMy8QDmzAiV4FfeZ7rJAMCp8rjhCY6eN\nho6hH6OSPAZXlYeH3DfaSBI4k02cyVGSZ0WJtcsDFhJde+xISlyQ8OVreHJ0RHxZwcmS4i/m0vL7\naO+pZkvt86zb+99srnma8qzrKcn4Auox7u1VrTbe3OLCYuhV/R2Ndlx2iyvLjx8wWSAQnHsIEUIg\nGANWNFcdJUAczqqDdfxp76fcklEyhr0SCEYPm2Rjiq+QKb5Cvpl9LfsjTQOCxK+qn8aGTLmvIB5H\nIlBOqmPkEZUFgjONXYFb5vawbp+DzbV2uiPxpVyX3WRatsa8gigu+9h7u0oSOBJNHIlRkmdGiXbI\nhPoEiYb37Ug2C0+2jm+ChidXZNYQnBwJnnwWltzL9LyvsK3uRTbtX87WuucpzbyO0sxrcar+QeXX\n7HUeU4Do57NaOxcXRfF6T2fPBQLBeEOIEALBacayLP7WWHnCcq/V7+LLqYW4bGKZSnB2I0kS+a4M\n8l0ZLMu4jLZYJ+s644Et/1z3Oo/WvkKBK5O5fYEtC1yZJ65UIBhnqDa4eGKUCwujdIRlLAsCLhNl\n5LH7Rh1H0MQxPUrS9CixLolQddxlo/FDN5Js4c8DV46KN1fHdppcRwTnHn5XJhdOvIdpuTezve5v\nbKt7ie11f6M442rKs67H7UiiOyKxv+3E0wzDlKhoVElLGoOOCwSCcYMQIQSC08zunnYaIicOWhQ2\ndNa3N7IweWS++gLBeCXJHuALKRfyhZQL6TUifNK5i7Ud23i1+SOeblxBij3IxckXMNMziXJfAcop\nRGAXCMYaWYZEzyinHDwN2P0WiVNjJE6NoYXigkRvrZOmv7tAAndm3ELCm6ejuIQgITgxHkcycwrv\nYGrOUioaXmVHw+vsaHiNiWmXkxZcAvhPWAdAT1QEYRQIzjeECCEQnGa69OhpKSsQnI24bU4WJE5j\nQeI0NFNne2gf6zoq+LjtM/7W8CEem4tZgRLmBsqZGSjGbXOe6S4LBOccqtciYXKMnHl2Opp76K5W\nCVWrHPjYxYGP40EvfRPiqT9VjxAkBMfHaQ8wI/9WJmffwM7GN6iof4XKpncIOC+nJ/YVdHPCcY93\nqeIaEwjON4QIIRCcZoLq8CdRJ1NWIDjbUWWFC/yTuMA/iX/2fIUtLZWs7djGuo7trDy4CUWyMdVX\nGHfbCJSTZA+c6S4LBOccitsioSxGQlkMPSIR2q8QqlJpXuekeY0LZ2rcQsKXr6H6xGRRcGzsioep\nOUspy7yO3U0rWLfvJdzqO0S0SwjFbkUzj457JUkWpRkaYB/7DgsEgjOGECFOI8888wzV1dUUFBSw\ndOnSM90dwRmiyJNAjstHbbj7hGU3tjdS5EkgwykiNAnOLyRJotCdRaE7i69kXsmB6EHWdVawrmM7\n/13zCn/gbxS5s5kXnMy8YDm5zjSRR10gGGUUp0WwWCNYrGFEIVQTt5Bo3eikZZ0LR7KOb4KOL1/D\nHhj/LiiCM4Nic1KadS3IX+SdrR/jdSwn2XMHUX02odhtxIxp0BewsixDwy/cfwSC8w4hQpxG5s2b\nx4wZM9i8efOZ7orgDHNjZgn/tXfDccvMSsxkS1cLH332LguTc1iSVUKm0zdGPRQIxhdpjkSuS72Y\n61IvJqT3srFzJ2s7tvNi0wcsb3ibdHviQGDLMm8+NhFHQiAYVWwOCEzUCEzUMGMQqlUJVSu0bXLQ\nusGJPSHusmErB8sez8whEBxOaYZEZ+8iPth1JU5lJV77EyS57yGmTyEUu5W0wCyunjI2qWwFAsH4\nQogQp5H8/Hyqq6vPdDcE44CFybm0xcI8UbuNofT+y1Py+f6USzjY3TWQznNlaw0LknJZklVMtmt4\nwZ0EgnMRr+Lmc0kz+FzSDDRT57PuPazr2M5H7Z/xSvNH+GxuZgdLmRcoZ7q/GKdNmPUKBKOJbAd/\noYa/UMPUw/TUKXRXqbRvddD2qYQ94MXb57LhSDKFICEYYF5hjPxknU/3L6Cq9XNo5ho89iewK9/H\nay+g/uBScpPnn+luCgSnhccee4zly5ezYsWKM92VcYcQIQSCMeKGzGLmJGTw9oF9bO9uxbQsCjwJ\nXJ1WwCRvIpIk4ZBtfDG9iCtSJ7CiuYqXGir5e1sNFyflsDSrhBwhRgjOc1RZYVaghFmBEu60vsye\n3jrWdmxnbcd23m/7BLukMM0/kbnBcuYEykhQhTWRQDCayAr48nV8+TqmEcY66KW5wqBjh52Dm52o\nPgNvfjyOhDPFEIKEgPSAyRem9ls8TMGyfklT51a21D7HhzsfxO/KZnbRV8jyz0eWj56aNHVsZV/L\nSqJaF057kMLUz5PqLx3bkxAIRshIXEdXrlzJokWL8Hg8ACQlJfG9732Pu+66a8j9KSkpLFq0iPvu\nu4/c3HiWvccee4zbb78dl8uFZVlIksQDDzzA3XffPUpndupKU5oAACAASURBVGoIEaKP/fv38/HH\nH9PY2Eh3dzc33XQTJSWDA+isX7+e1atXEwqFSEtL4wtf+AJZWVlnqMeCs5Fsl5/b8y84YTm7bOOa\nPjHivZZqXmzYxXe2rOCixGyWZpWS6xZihEAgSzKTPLlM8uRyW9bVNERaWff/s3ff8VWW9//HX/fZ\nMzk5WedkkISwZW9RhqJ1dyEqfFscoLbfVmvVDqtWkda2X/srXeKoWreignvXgUVlKYhCIIzsPQ8n\n86z798cJhwQSCCE7n+fjwSOHc1/3fV/3dZIz3ucanl1srt3Fmrx13M86RluHMbtl2EaKKaGvqyzE\noKLRgi0DtPGNqCFoKNbhzdVxaF+4l4TOGsKe7seW4cecKIGECFMUBbdjIm7HRCoO7WVn4Yt88PWf\nsBrjGZ+yiJGJ56LTGmn01fDh7t9T4d3TZv+9JW/hip7IWWNvwyhB84Cw4PUH8YeCPXoOvUbLx5f8\nqEfP0ZsyMzPJzs4GYPfu3Zx55pnMmzePCRMmtNmuqioHDx7knnvuYfr06Wzfvj3y+fSss87ivffe\n67NrOB5NX1egv/D5fLhcLi666KJ2E6tvvvmGd999lwULFnD99dfjcrl46qmnqK+vj5TZsmULDz74\nIA8++CCBQKA3qy8GKb1GywWJmTww6Tx+lD6FvXXV/Ozr9/m/fZvIbfD0dfWE6FeSTHF8L3E+fxz9\nvzw58bfcmLaYaJ2NZ4vf58e77uNH3/wfjxe+SVZdLiFVJtUTojspGrCmBHCd2UTmUi+pF9ZhS/Pj\nzdFT8IaNA8/aKfvUxKECkD8/cVh81GgWjruDJWc+RmLUOLYceJiXti5nZ/4LvPv1HccEEIeVenby\nn113E1J79oOt6B7+UBB/KNTD/07+dyE3N5eLL76YuLg4kpOT+ec//0lhYSFRUVE0NTVFyj355JN8\n61vfAqC2tpYlS5YQHx/PyJEjefjhhyPlGhoa+J//+R9iYmKYPn06+/bta3O+DRs2MH36dGJiYjj7\n7LM5ePBgp+o5btw4JkyYwJ49x/49KIpCZmYmTzzxBMOGDeMvf/nLSbdDX5CeEC1GjhzJyJEjAVDV\nY0ftf/7550yfPp3Jk8PfYl988cVkZ2ezfft2zjzzTABmzpzJzJkz2+zX3rGEOFl6jZbzEodzdnw6\nH1Xm8VLxHm76+j/Mjkni8uSxZFgdfV1FIfqVaL2Nc+JmcE7cDJpCPr46tI/Ntbv4T9VW1pV9jENn\nY07cRKZZRzMpaiRGjb6vqyzEoKFowJIUxJIUJOH0JprKtXhz9Hhz9dRmadCa7NiGhYdsWJICyLyy\nItY+nHljfsHktB/wTeFLbM97GpXjp1UV3r0UVG0hTeaUEF0QDAa56KKLuOKKK1i/fj0+n499+/aR\nkpLCpEmTePPNN1m0aBEAa9eu5YorrgDgJz/5CRqNhsLCQrKzs1m4cCFjx45l7ty53H333VRUVFBY\nWEhhYSHnnntupGd9QUEBixcv5pVXXuH0009nzZo1XHHFFWzZsuWEdf3qq6/Iyspi6tSpxy13ySWX\n8Pbbb59iy/SOLocQwWCQF198kY8++ojy8nLuueceJkyYgMfj4YMPPuCMM84gMTGxO+vaZ4LBICUl\nJcydOzdyn6IoDB8+nMLCwg73e/LJJykrK8Pn8/GXv/yFyy67jJSUlMj2VatWdbjvHXfcgdvt7p4L\n6CS7vfe7tHXHOU/2GJ0t39vtD52r25XJyfzPhFm8VbCXx/du4+fffMB893CWj57BaEd8j5yzu4/R\nmfL9tf374zml/U8sIzmN73IOQTXE1zX72VD6BRvKvuCt0s8waY2cHj+B+YlTOTNxCg5DzzynnGx5\neQ3omWP01/aHQfwYJAGTw1/MeEtCVGYFqdijpTDbgM4EsaO0xI/REpN5JI2Q9u+5Y/Tn1wA3bkYP\nn8rj7xdSUrPrhPvk125g9oTvn9I5T1VPPQeJnrV582bq6uq48847ATAYDEyZMgWAyy67jLVr17Jo\n0SJqamrYsGEDzzzzDKFQiJdeeol9+/ZhNBqZMGECK1as4Nlnn2Xu3Lm89NJLPPHEE1itVkaPHs2V\nV17J5s2bAXj22Wf5/ve/z5w5c4BwmLFy5Ury8/Mj8zi0dvDgQZxOJ4FAgPr6en72s5+RmZl53Gty\nuVzU1NRE/v/xxx/jdDojc0JkZ2cTFxfXLe13qroUQtTW1nL++eezZcsWbDYb9fX13HDDDQDYbDZu\nvPFGli1bxr333tutle0rDQ0NhEIhbDZbm/ttNhtVVVUd7rds2bKerpoYonQaLd9OG8eFqaN5pyCb\nf2dvY9nHa5nrymD5mBmMdcjYdyHao1U0THaOYrJzFD8bt4TcumI+Lt3GhtIvWfnVv1CAyc7RzHdN\nY37iNFKs8rckRHdRFIWoJC1RSVoyzlapL1epyApQsSdI2c4gWgPEjtQSN0ZL7AgtWoNMIjFU1TVV\ndqqcp76kh2siBqvCwkLS0tLa3bZ48WLuuOMOGhsbWb9+PfPnz8fhcFBeXk4gECA1NTVSNi0tjV27\nwoFZSUlJmy+cU1NTIyFEfn4+Tz75JC+88AIQDmUDgQBFRUXthhDDhw+PzAlRVFTEhRdeyP333x+Z\nnLI9JSUlxMTERP6/YMGCfjsnRJdCiF//+tfs2rWLd999lylTppCQcORNmlar5dJLL+Wtt94aNCFE\nTzmcvLVHVVVKSnrvidVms1FXV9dr5+uuc57sMTpT/nD635vtD11vj2kGB5NPO5tPKgt4sXgPV338\nAtMdLi5PHstIm7NHznkqxzhR+YHW/n19Tmn/UzunsU7hPOsMzsucQY3/EFtqs9jk2cU/s9ayevcz\npJlczGqZ2HKEJRmNojnmGIPhOUh+/48Yan8Dff0YmEZD6mhortVQl6OnodDM7nVBFK2KNTWAPd2P\ndZifnl55d6i2/9H6y++/hk4OkVO1Xa5rf2x/6JteKENRamoqeXl57W5zuVxMnz6d1157jRdffJGl\nS5cCEBcXh16vJz8/PxJg5Ofnk5SUBIQfu4KCAjIyMoDwEIzDkpOTue666/jrX/960nVNTk7mvPPO\n49133z1uCPHmm28yf/78kz5+X+jSxJSvvPIKN9xwA+eee267kziOGjWK3NzcU61bv2GxWNBoNMc8\nadTV1R3TO0KIvqBVNJwVn8Y/Jp7LzzNnUNJUxy92fcQ9ezaSXVfd19UTYkCI0UdxXvws7hpxDc9M\nupvbhi9juCWJtys+55Y9f+fqr3/Pmrx1fOHZgz8kkw8L0Z2MjhCxU5qZfp2ZWT8xETetiUC9QsnH\nFg48HUXhuxY82XqCTdI7YigYFju7k+VkPoiBQK/RotdoevjfyU0uM3PmTOx2O7/73e9obm7G6/Xy\n5ZdfRrZffvnlrFmzhk8//ZTvfve7AGg0Gi699NJIL4lvvvmGRx99lCVLlgBw6aWXcu+99+L1etm7\ndy9PPvlk5HhLly7lxRdfZOPGjeGhaV4v69at67B+recVLCkp4b333mPcuHHHbA+FQuzfv5+rr76a\nvLw8brrpppNqh77SpZ4QHo8nkvC0x+/3D6rVIbRaLW63m5ycnMjkIqqqkpOTw6xZs/q4dkIcoVU0\nzI8bxpmxqXxaVcgLRVn8ctdHTIlO5PLksYyxx/Z1FYUYEMxaI3NiJjAnZgJBNcjuulw21YaX/3y7\nchNmjZFp0WOYnziV8cYMbDpzX1dZiEHD7NTgnOjDOdGHv07Bm6unLkdP6SdmUMCSFJ7U0pYWQGeW\nCcAHo9HuC9ld/BrBkK/DMjqtmVGJ3+rFWomu6o9LZ2q1Wt544w1+/OMf85e//AWz2cztt98emfxx\n0aJF3HDDDVxwwQVERUVF9vvHP/7Bj3/8Y1JTU3E4HNxzzz3MmzcPgLvuuotrr72WYcOGkZmZybJl\nyyLDMdLT03n++ef5xS9+wd69e7FarZx99tmRyS+PlpOTEzmvxWLhkksu4be//e0x21VVJT4+nrPP\nPputW7e2GQ7Sn3UphMjMzGyTFB3t6KRmIPD5fFRXV0dSpZqaGkpLSzGbzURHR3P66afzyiuv4Ha7\nSU5OZtOmTfj9/shqGUL0J1pFYV5cKmfEpvB5dSEvFO3h17s/ZlJUApenjGWcvX9MSiPEQKBVtEyw\nZzLBnsmKlEvIayplU+03bK7dze/3/BstGsbbh0eGbSQYYk58UCFEp+htKs7xPpzjfQQaWgKJXD1l\nn5op+xTMriD2dD/2dD86qwQSg4XNlMC80b9gw57/I6T6j9mu0xg5a+xtmAzRfVA7MVikp6d3uJpE\nbGwszc3Nx9wfExPD888/3+4+VquVZ599tsPzzZ07l88///yE9Zo/f/5xv9A/0XaAK6+8kiuvvPKE\n5+orXQohVqxYwa9+9SsWLFjAwoULgfBkQ83Nzdxzzz288847bdZMHQiKi4t5/PHHURQFRVEik3hM\nmjSJ7373u4wfP56GhgY++ugj6uvrcblc/OAHP8BqtfZxzYXomFZRODM2lTnOFD6vLmJtURa/2b2B\nCVHxXJE8llkynEiIk6IoCulmN+lmN1e4z6VR7+fDoq1s9uziscI3eLjgVYabk5jdEkhkmJPaHbYo\nhDh5OotKzDgfMeN8BBoV6vJ01OXqKd9kovxzM6aEcA8Je7ofvV0CiYEuLe50Lp7yF3YXvUpOxX8J\nhprRac0Mj5/PuOTv4LCknvggQoh+qUshxM9+9jN27drFkiVLcDgcQHicS1VVFYFAgOuvv57ly5d3\na0V7Wnp6Onffffdxy8ycOZOZM2f2ToWE6EYaReGM2BROdyazuaaYtUVZ3J71CZNKsrnUPYoJUSe/\ntKcQAuKNMVyUMIeLEuZQH2zkS89eNtXu4pWy//JsyfskGGKYGT2O2Y7TOM0+vK+rK8SgoTOrOMb4\ncYzxE2yGuvzwkI3KbSYqNpsxxQWwZfixpwcwRIf6urqii5zWDM4cdRNnjLyRQKgZncYkwa4Qg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QAE5UP1M4LGgdBOhaffBvCQvaCxQ63KclLGivh0l4PoeGzrVZF0WP8VH9tRE10PG1a/Qq0aO6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\nH3g8HrvLAIKKEKKF4ukYwRmDO6M3/5zcGd3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f671a253518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (-9223363281804963012)>\n"
     ]
    },
    {
     "data": {
      "image/png": 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ouuq3EfC3Qw17IUS423ySbIwEKKwwRAIVXQMXitEWOWbtTEvIYx3w9kuShGxb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JbFr06NPb3RBH51yj+uoG15vJhZ2ZCGCAEgNZ9FGRjQpwzeLVVNDPhHGvjLSQEAWcHmchKJ6UPL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\nX0N82WrUC+cxDu7DvXMH7rdfI1Uxi8SCJ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iBcE01WuTl7MTdnL+ZCvImXO97lxbq3eK7x\ndfL0LJqS7Zdd18Hh/5x8huUL/yfGdZSAH7T151mNSLVGww43DRKi9ecV5Ppt5pfA26dcLCpJIYsa\nG8IEEW+WaXjDTWB2kmBlcqwPZ9RcrpNGaFGC0IKE6KQhCIIgTEgiCCEIwnUrduXx/+Zu5k/ybmVH\n236erP7lVdfpMqPsaN3H7bkrb3j/mt8htCBJaEESMyoROaf2a/1p5Jr4y018ZSmM7InTwm8kbZxr\n89Q2hUO1GgtLRDaEMP5ZcYkL273o2Rbhm2JTslDjpZ00WvcatB/WRScNQRAEYUISQQhBEG6YLmvM\n91VgMbQL/cORam7LWTGsYx5VjzOg9WekWs20/tSDVrrTRrk5pVt/FmXDzPwUb50ymF+cQp6i50GY\nGBwbal9x45hQfHt0yhek7e2kkT0vQdM7LtFJQxAEQZiQpvi/c0EQhot8DZ9+t7Xs5o22/YSNEAVG\niAI9hwIjRNjIYaHfocidd0PH0r/1Z4zohXRRy/ajOq37XVO+9ee6mQl+8paPo3Ua84pENoQwfjXv\nMYjWqpRs6hbDqy6i+R2KbokRX5igabeb2m1e3GGTvNVx3PmTu16GIAiCMPGJIIQgCMMiT8uiQA9R\nn2y96rKbw+8jqPmoT7RQn2ilquMIDclWTMeCkyAhkaMFCBvp4MSlgYos1TfkLApZBV+Zia/MxLEh\nVq/QVa0ROaPRfshAMWx8ZSa5lSCHpkbrz6Isi+m5Kd46aTC3MCXunk5Q8RTUdyjYjkS+38LnmlwX\n6V1nVFr3u8hbFcNbJC6sB+PKtSnZ1J3ppHHutz5801PkrRj+ThqCIAiCMFymwMdtQRBGgyRJbMpb\ny48v/P6Ky+Ub2XyseBPKJekHlmPTmuok5YML0UaONZymPtlKTbyRPR1HaTf7WloZstYvKJEOVORQ\noIfIN0KXLXopyeApsvAUWeSvjadbf55V6arWOHVcQdICmdaf3tIUyjW0/nQcSLTK2EkJ1Wej+8f3\nBeG6mQl+ttPH8QaVOQXmWB+OcA1iSYlXjxkcqtVJWekIkiw5zAqbvG9OnJB34l98Jtpk6l734J+e\nJHvh1ClEeT36ddI4qdG8p6eTxtyeThqiY5AgCMKgysvLaWpqQlEUAoEAmzdv5oknnkCSJG655RYe\neugh7r//fgCefvppPv/5z7NlyxY8Hg+PPfYYO3fuBGDDhg08+eSTmRa3X/3qV/nRj35EZ2cn4XCY\nL3/5y3ziE58Ys8c5HokghCAIw+ae/JvZ03mM97pODjpflzT+uvKBAQEIAEWSydOzKMwpZHnOXOrU\n2f3mx6wEDcnWTPZEQ6KVukQLezqO9mVR9AhpgUxQ4tJARbbqR5Kk/q0/VyRQkz4aD6euq/Vn22Gd\ntoM6qU4lM81daJK7NI5nnN7BLQ1ZTAuZvHnSxexwRGRDTBCxpMRPd3ppiSj9ptuOxLF6jXOtCh9b\n002Ob+IGIqwk1G73oPlsCjZMzUKU10OSITg7hb8iRduh9NCzjhOik4YgCGPv8JPrceyRHf4pyRrz\nHtlxbetIEtu3b2ft2rWcPn2aDRs2sHDhQh588MF+y/UGILZu3crixYupqqpi8+bN/OxnP8PtdvPY\nY4/xwAMPsHXrVgA+9rGP8aUvfQm3283JkyfZsGEDq1atYv78obeon+xEEEIQhGGjySpfnflJflG3\nja3Nu+g0u4H08IqlgVn8RdFdLMmaTSQSucqWBnIrBuXuQsrdhQPm2T1ZFPWJniBFspWGRAu1iWbe\n7TzWL4tCl7S+WhSZQEUO07NL8C92kbNUv6bWn/VvuOg4agw4plidyvl6L0W3RvFPH5+ZButmxvnF\nbh+nmlRm5o/PYxT6e/WYMSAAcbFYUubFA27+Ym33KB7V8HEcqH/NgxmVKftQRFw4XwdZhZzFSbLm\npGjZJzppCIIw9hw7hWONzxpUjpP+TFdRUcG6devYt29fv/mXBiAAVq5cycqVfV3ePvOZz7BixYrM\n7zNmzBiw/TNnzoggxEVEEEIQhGGlySofL97EnxXewbHucySdFMVGHmEjNGL7lCWZXD2LXD2LBf6K\nAfPjVrJfFkVvoGJf53EaEq0knb4L8GzVn86e8OZQsCxEEQWEG8sx6/Jourj1Z5mJrNuDBiAyHIm6\n1zx4ijpRrrDYWCnLsSjONnnzpMGMPFFZf7yLp+BQ7dXHCJ1vU2nslMkPTLxsiNZ9BpGzGsV3dIua\nBjdIcTnkr4mTPb9/J428VXG8opOGIAhCP8ePH2fHjh188YtfzEx75pln2LlzZ78AxGBee+21AQGG\nv//7v+frX/860WiUFStWcPvtt4/YsU9EIgghCMKI0GR10IDAWHApOmXuAsrcBQPm2Y5NW6qLTiXG\nmfaaviBFopX3uk7SmupML5gPvlwfq6KrWdC+hOJ9FSiWi4QcJyUl0W0D3RkYaXBMKZ0SvWD8jWuX\nJFg3I8Ez73ipblGYnjs+h45MdbaTHoZxslHJ1IC4mvOtKvmB8feau5Lu8yrNewxylsbxlYnMnOFy\naSeNC9u8uAtM8lbF8fnG+ugEQRDG1qZNm7Btm+7ubjZv3szDDz+cmffKK69w0003sWjRosuuf/Lk\nSR5//HGeeeaZftO/9KUv8aUvfYmqqipefvlldP0aCo1NASIIIQjClCZLMjl6kDJfMdOVgUGKuJ2k\nMdHWb5jHzsSLNMU68DXmM7djEfM7FqM7Bh1qO+e91djYqI6KYblwW26SZ714ShU0F8g64+oOZEWe\nSUEwXRtieu7ETOGfaCwboknpoi8583MsIWEfThCJObRHfOlpKQm4thfN8QYVr2FTmGURcDnj6jU3\nmGSnRO2rbrylJjnLEmN9OJNSppNGjUpTVbqTRucsh+wlssg6EQRhytqyZQtr167lhRde4NFHHyUS\niRAKpbN3v/3tb/PDH/6QBx54gJ/85CcD1q2treXOO+/km9/8Jhs3bhx0+ytXruSnP/0p3//+9/n0\npz89oo9lIhFBCEEQhCtwyTrT3GGmucP9pjsOHPuRny6liyajgRr5HC7bTXF0Gpqt4bJdqE7PgPY6\nOPvL9I82NqaWxNFTqO4Yqi7j9qgYLhnFcFBcDrLhoBg2istJTzMcJHX4ghcdqQjbWqo4Fb2AjERp\n3hrOnFzGuVaFwoElN4SrSJoMDCYMEmBIBxRkEqnggG2osoNHT39l+R2yfRIht9kzzcajOyDBr/d4\ncIYQkGjsUvjNXi8AHt2mIGhReNHXeGrnaZtQu92LYjgUvi867gMmE5kkgbfUxFOc7qTRstdNu+ik\nIQjCFNZbs+Gee+7h+eef5xvf+Abf+c53AAgGg2zdupX169fzyCOP8OSTT2bWa25u5o477uDTn/70\ngEKWlzJNk5MnBy/aPlWJIIQgCMJ1kCRwhRykliABc+BFJUCKFB16K1Z5C6mcLrqicaJRi2TCxopL\nGKYbT8SLp8OL3/LjtXwYphvZGVg5TlJ6gxMXfbkcXD4JS9b7BSz6BS8uqWH4zPnt/NuZ5/p1E8HZ\nz1L1K/z3EQ+rx1nNJDsFHSd0Ok9omN0ysu7gr0iRVZlE9Qz/BZOmqvdWAAAgAElEQVTjQMJkQPAg\nmpCJpS7+PR1giKUkUtbA519X+4IHHt0h15f+OcunoRDLTO9dRlP6gky9Lb7q6loHbHdOQYqj9VdO\n6czzWzy4PkIkLlHXoVDfoVDXobD3nM6byfRry2fYFAatTHBi5hh9GnAcqN/hJtkpU3ZvZFzWTpmM\nejtpFC4yOL8rmemkkbMoQbbopCEIwjCTRuFNZTj28dhjj7Fq1Soef/zxzLScnBy2bdvG+vXrCQQC\nfPOb36Srq4s777yTe+65hy984QsDtvODH/yAj370owQCAV599VV+/vOf84tf/OKGj28yEUEIQRCE\n65RVmaThTfdl52to5Kbymb7Uje7vf8HsOA6m4XCq9Tz1yRpqeotmxltoi0aJxy08pge35cVvBQiT\nR64TItvOwm8F8MS96F1uonUqVtyFnRz89rGk9QUk2qQ2uswQ96p/QlTpplvtJqpEiKrdWFIVqZYP\n8bPXawi684hEDAqzLGblm8hjVE0/2SlR86KXVNdFkZQotLyr0HbAoPiO7qu2QLUdiPcb+tCXnZBy\nJDq63cQuCTjYzsBz6dLsiwIHDgVBC49uku3XUZx0UMF9UdBBvUwDC59PJRK5/grh76uMc65VJZoc\n/ElRZIf3z4+l9+VymOUymRVO11dwHOi6KDBR26FQVa0TT6W3FXT7M0GJgqBJQdDCPcKfG9sP6XSd\n0im8JYoREkMCRtulnTRa9hq0HdHJXSY6aQiCMHyutXXmaJEuSb2rrKxk48aNfPe73+03r7i4mG3b\ntrFhwwaysrIIh8Ps27ePEydO8NRTT2W21dmZriP2u9/9ji9/+cukUimmTZvGP/3TP7Fp06bRe2AT\ngAhCCIIgXKfArCQdJzTijZd/Kw0tSgwIQED6n1W27qfSV0YlZQPmp2yTxmRbX0ePZAsnEmcyv8fs\nvnHzfsVDgZ5DqVxEkRQmTB45ToignYXH9OIkZcw4VDfU4DF95CXCeEwfHsuDYbv677gxhIOFIbmI\nyPCO6hAMWvj99mWzLWSXnf6uDd+QEceGmq2XBCAAG0jIkHAkGl71ElgTIyH31VPIZDCk+oZFXFpP\nQZIcPJqDzy3hUmU8uk3I5wzITuj9cmvOZQMxPp92Q0GFa5XtcfiLNd28eNDN+db+r7s8v8X758eY\nFho8MCNJEHA7BNwmcwr6AhPtMYn2hI8zDSnq2hXeOmWQNF09+7P6DeUIBy2MYfrk0FUDjbtcZC9I\nEJgxPlu3TRW9nTSy5ido7u2kcdAib6XopCEIwuR1+vTpAdNeeOGFQZedMWMGFy5cyPz+8Y9//LLb\nfe6552784CY5EYQQBEG4TrIKpXd1U/+mm67TGlx0B13WHUKLE+Qsvr4ie5qsUuzKo9iVN2Ce4zh0\nWlEaEi20S91Ud9RSn2ilIdnMwcRxmpPt2KQDHzIy+UYWLo9BtbtuwLZUWyUvNpcFbZ/GbWvoNv2/\nHInuToViS0KzwYpLWAkJ7EGuSqT+QQp5kICF4rL7TbcUh4Qj0Z6ElnY1k43Q3qjQgkIiBEk5HXhI\nypDqFwyQ4KgnU0/B3RNACLhtCoIDAwpu3cFj2Lh66mv4fD4ikYlXjDPHZ/MXa7pp6pI516riOFAQ\nsCi5TPDhSiQpHdgozXeYnh0H0oGJ1m6531COk41aT2cOhxxf/6Ec4YCFdpnMj8tJdUuc+28Zd0G6\nZaQwPuhX6KThzhfdcwRBEIThITm91TiEcaWrqwvx1IyN3vQrcf7HxkQ9/8kItJ+SsBKgByB7hjNm\n46pTtkljopXaWDN18WZqY81UtR3mdPeFQZcPxZdRENtAIDkb+TKx6YKgwyN3ptPlHSddq8GMgxVP\nfzdjEmYc4t3pr2QMUjEJKw5OAkiCZII0SFFFi3SAoffLVBwsCSxHwgEc0rkMcs9LQnPAsNNfXrfD\nsk/ak+pO7Xj8G7BsaO6CC60SNW3p7/XtYNoSsuSQH4DikENxdvp7QZDLDkmxTTj+rEwyAnPvt9E8\no/tYrmY8nv+x4DjQeRYuvCETa5bInmVTtM7BlTWy+xXnf2yJ8z/2JEnC7/eP9WEIwogSQYhxynEc\n6uoG3rUcKek7gpFR299w7fNatzGU5fuKwo3e+QfxHPQS539ktrGlaSdPnfvVgOmK7UZxXCSVNhTb\nxbTIRyiMvY8UETR8/ZbdMDuGW+vfXvKa6iloDj7FwSc7eCUHlwOGk8648CgadsxENsFOSMQaVBxz\nCJEF2WHO/9N59eUGMR5f/zA2fwPXcy4sG5q65Ey2RF2HSlOXjO30BCb8PfUlstLfc302igz1b7jo\nPK4z549tHJ84/73G63uQY0PnSY3mPS7MqDSgk4b4HzC2+xTnf2z3OVLvQZfWKhCEyUYMxxAEQZgC\nVgbnoiBj0b/4XzBZyZyOTxNVa2g13qPZ2EUosRDV9uNgI9E3/uH14+5MPQWP7uAxnL56CpdMG0o9\nhYulCzbGMr9feMlD5MwQUklsiTPP+nAXmHgKLNwFJppPxNZHgyJDQdCmIGizhHRNB9NKtwftHcpR\n06ay77yOg4QqO8y3bGbUKaQWJOh2q7gckMVn7XGtt5OGvyJF2yF9QCcNQRAEQbhWIgghCIIwBeTo\nQdZlL+L1tn39pjuSiYSE1yzFa5ZSyt2kpG5iah1dWjWd2nGSShs4EnP9xdxZWsHiwEyMER5rEpiZ\nHFIQwleWRHE7xOpUOo6m+zuqPhtPgYm7wMQdttCzJtdwjfFMVaAoy6Ioq69+QNKExk6FhvMqnncM\nagM2O1sN2AqaEqAgcFHxyyyLbI94vsajSztpNPd00iheC64yRCcNQRAEYchEEEIQBGGKeLjsIzSY\nbRzrOpuZ1qmdxCKBgpGZpjleNNOLzywjHFtHp36cduM9qp39fP3Uq+iSxuLATFYG57IyOJdcffgH\niftKTYyQRaL18hUPZcMmfFMc1ZvOfDBjErEGhVi9SrReofOUGxwJxWXjDqezJDyFJkbIFhdMo0hX\nocBtkziso+ZabPxgNzfZ0JHycaY+SV2HwolGlarq9GvQUJ1+rUILgxZBtyMCE+PEpZ00zr2ko2f5\nRCcNQRAEYchEEEIQBGGK8Cpunlj8OX5V/RIvNr3NhUQTlhwj5T+I0rV80HVkVLKS81gTmsmHFt5F\nTaKJqvbDVHUc4V/OPcf/5ddUuIvSAYmseczylCAPwxW+JEPxnd3UbPGSbBsYiFBcNsXvj2YCEACq\n28FfbuIvT7eftJMQa0wHJGL1Ks3vuHAsCUlzcIdNsqZJKNkKrjwLWfw3HDGODXUve3AsKLo9iqyA\noUBFNuR7kpnlYkkpU1+ivkPhcK3GztPpwIRbs/u1Ci3MsvAZYtjNWOrtpCGtUjj3miM6aQiCIAhD\nJj52CYIgTCEuRee+8HruC68nbidRkLEtlZ/vsqjvHDzrIOS1uHN+HEmSKHXlU1qQz0cK3kfEjPJu\n53F2dxzm901v8V/1L5Gl+ljRkyGxJDALj+K67mPVvA7lH4rQVa3RcVzDjMoouoN/eorA7CSKfuX1\nZR28JSbeEhNIYFsQb0oHJGL1CvXvSNhJH5Ls4MqzMnUlXGHzqtsWhq6pykW0XqH0A91o3ssHDty6\nw/Q8k+l5ZmZad0Lq1yp0/3mdt06lg1xew6YkJJHnMzLBCa8ITIw6bxhKNnUTrVFp3O3i3G99+Kcn\nyV2RQA/aV9+AIAiCMOWIIIQgCMIU5ZJ7rrRl+PM1Ed4+ZXCg1kVXT31Il2azqCTFTTMSuPWBF3c+\n1cOG0BI2hJZgORZHI2ep6jjC7o4jbG+pQpUUFvgqWJk1j1XBuRQYOdd8jJICgRkpAjNSN/JQAZAV\n8BRYeArSd2m9Hh8t56I9wzdUOo7ptO6XQXIwQhfVlSiwMp0AhGvTeUqj7YBB/poYnsJrvzvuNRxm\n5pvMzE8HJhwHIj2BiboOhaaIwZ5qnVgqHZgIuOyLhnKkvw/22hWGlySBt9SkvDiS6aRx5lltQCcN\nQRAEQQARhBAEQRBIj9vfOCfBH63PprnLoamxiSyPjXr5kgz9KJLCfH8F8/0VPFByN/WJFqo6jlDV\ncYQf1/yOfzv/PKWu/J46EvOY6ytDkYa48REiyeDKtXHlJslekMRxINUpE61TiDWoRM5qtB1KDwfQ\ngxbugt5sCRPVJ2oUXE2iVaZ+hxv/jCRZ85NXX2EIJAn8Lge/y2R22MTn0+jqitARu3goh8rO0waJ\nnhavWZ6+oERFAQQ1MIaxrmo0KVHbnn4thwMWftfUveAe0Eljn4vOEzqhnk4aI1zPVhAEQZggRBBC\nEARByFAViYIsCSd2Y2nUBUYO9+TfzD35NxO14uzvPEFVxxFebtnDrxtew6e4WRaYw6qseSwLzMGH\nb5gewfWTJNCDNnrQJqsynXmR6pYywzd6syUAVK+Nu8Aku0xCzpZFB45LWAm4sN2DHrApWB8b0XMj\nSZDlccjymFQWpofeOA60ReV+QzneOKHxylEJCBLy9s+WCAcs9Gv8RBRJSLx8xMXReg3LTj9AWUpn\nbtxaGSfbO3WHIvR20gjOSdF6USeN3GVxvMvG+ugEQRDSysvLaWpqQlEUAoEAmzdv5oknnkCSJG65\n5RYeeugh7r//fgCefvppPv/5z7NlyxaWLFmS2ca3v/1t/uZv/oY33niDm266CYBPfOIT/OIXv0DX\ndRzHoby8nAMHDozJYxyvRBBCEARBGFEexcXa7IWszV6I7dicjNawu+MIVe1HeP3Mz5GRWRCcwXLf\nbFYG51LiykcaJ1f0mtdBu2g4iBVPd+CI9gQmzr0igeNHNmw8BRbucHr4hivXmrIdOBwH6l71YMVl\nSj7UNSZFPyUJQl6bkNdmflH6ubMdiNs+TtUlMsGJY/Uapi0h4ZDrSw/lmNOWYlqujGJx2UygSELi\np2/5aI/1f5JtR+J4g0ZNm8LH1nYTmsKBCAC1t5PGvATNe1w0vOGh47BDznJVdNIQhCnkhf+4Gdu+\n8WGVVyLLGvd8/I1rWkeSJLZv387atWs5ffo0GzZsYOHChTz44IP9lusNQGzdupXFixdnptfW1vKf\n//mfFBUVDdj2V77yFf7mb/7m+h7MFCCCEIIgCMKokSWZ2d5pzPZO4y+K7qQl2UFVxxH2dh/n6do/\n8OMLv6dAD7Eyax4rg3NZ4KtAG0etKxSXg6/MxFeWrlHg1n00n4n1ZEuoNO/p6cChOrjzTdyFFp6w\niSt/6nTgaNlr0H1epfjOKHpg/AxNkCXID4JHSbGwpCeoZENzRM5kS9R1KBzelcKyQZYC5Pntfq1C\n8/w2igwvH3ENCEBcLJqU2XrQzZ+t7h6thzeu6YF0J434wgSt7/hEJw1BmGJsOzXiQYjr5Tjp/1MV\nFRWsW7eOffv29Zt/uQAEwOc//3m+9rWv8dnPfvay2xUGN0U+EgmCIAjjUY4e5K68NfzR9Ntp6Wzj\nQNdJdncc4e22g7zQ+AZu2WBJYBargvNYHqwkW/OP9SH3o+jgLbbwFltAAseCeLOSaQvadsCgZY8L\nZAdXrkVwmoQaUnGHTRRjrI9++EXOqbS86yJ3eRxfqXn1FcaYIkM4YBMO2CwuTX9Azs0voK7V5sCp\nduo7FGrbFfbXaDiOhCKnMyYaO6+e5lLdotISkcnxTe1siIu5cm1mbbZpOBqnSXTSEARhHDl+/Dg7\nduzgi1/8YmbaM888w86dOwcNQLz66qu0tLRw3333DRqEeOKJJ3jiiSeYM2cO3/rWt9iwYcOIP4aJ\nRAQhBEEQhHHBkDVWBOeyIjgXp9ShOlaXKW753bO/BGC2t7SnuOVcpruLxs2wjV6SAu6whTtsweIk\njg2JNjlTV6L1iEKq2wukO3D0Frp0F1ionol91yTZIVP3qgdfWYrQksRYH8510xSJaXkKmtlXTDNl\nQUNnegjH8XoVh6EVVd1/XmNBcQq37uDWnCEXep3MJAl8pSbe3k4a7/TvpCEIgjCaNm3ahG3bdHd3\ns3nzZh5++OHMvFdeeYWbbrqJRYsW9VvHsiw+97nP8fTTTw+6zc9+9rM88cQTeL1ennnmGe69914O\nHDhAaWnpiD6WiUQEIQRBEIRxR5IkpnuKmO4p4o8Lb6M9FWFP51Gq2o/wq/pX+VntVnK0YCYgsSgw\ns6/l6DgiyeDKsXHlJMmeD16vQlttNFNXortGpf1wOiVCC/S0D600ySqVcRwmzJh5O5UuRKm4bQo2\nRifMcQ+VpkBJtkVJtkXQbXO2dWhtHnadcbHrjOui7Ti4tHRAojcw4dZsXLpDlk9CtjXces8yPfNd\nmoMyCeuL9O+kYdC6z6DzhI69LkXJGvHxVBCE0bFlyxbWrl3LCy+8wKOPPkokEiEUCgHpopM//OEP\neeCBB/jJT36SWeepp55i/fr1zJ07d9BtXpw1cf/99/PTn/6UP/zhD3zyk58c2QczgYh3eUEQBGHc\ny9J83JazgttyVpCyTQ5HzqSLW3YcZkvzTnRJZVFgFqt6Miny9KyxPuRBXdyBIzg7nf5vdktEG9KZ\nErF6lWO/Td+BVz1+3D1ZEp4CEz17fHbgcByof91NKiJTdm8EZfzFgoZVOGAhSQ6Oc/Un4yPLIvgM\niKUkYimJeFLK/BxLSsRTEu1RhVhKJm5KJFKeQbdjqL3Bi3TAon8Qw+mb1jPd1RO8kMfh6+VS6U4a\nCYJzkrTuM6h+XeLCHpPsJRrBWakpW+BVEITR0Vu74Z577uH555/nG9/4Bt/5zncACAaDbN26lfXr\n1/PII4/w5JNPAukMiR07dvDMM88A0NTUxH333ce3v/3tQQMNsiyLGhGXEEEIQRAEYULRZJXFgVks\nDsziwZJ7uJBoSg/baD/Cv5x7DptfM91dyMpgurjlbG8p8ji+klG9DoGKFIGKnpoEWQV0nLepPdJJ\ntF6h620NHAnZsHHnW3gKx1cHjrYDOl1ndIpu68bInvzj+gNuh5l5Jicar5wNUZRlMqdg6EUXfT4f\nHZ0R4pcJWPRNk4kmZVoiUmbZlDVYtKF/1sWlGRguzSE7ICFZas90G5fmYKhjk4HT20lj9vuCnHkl\nRcMOD20HLPJWxfGWik4agjCRyfLQssfGeh+PPfYYq1at4vHHH89My8nJYdu2baxfv55AIMA3v/lN\nfvKTnxCPxzPLrFixgu9973vcdtttAPz617/mrrvuwjAMnn32Wd544w2eeuqpGz6+yUQEIQRBEIQJ\nS5IkSlz5lLjy+XB4IxEzyt7O4+zuOMKLTW/zTP1LBFUvK3qGbSwNzMajuK6+4TGkuSVyZyuk/OkP\nOLYJ8cbetqAqze+6cEwJSXF66k+YuAtN3GPQgaO7VqGpykVoURz/9PFfiHK43Do3Tk27Qiw5eBRI\nUxzumBcfdN6VKDJ4DQevcW13zEyLftkV/YMXciag0RmXaejsW8ayJcDbb1uSNDCr4tLghVu3M9NM\nOf0a1ZThCV64s2XmfcTAPbORpt0uLvxh7DtpdMYkjtZrxFMSPsNhbmG6zocgCENzra0zR8uldaUq\nKyvZuHEj3/3ud/vNKy4uZtu2bWzYsIGsrCy+8IUvEAgEMvNVVSU7OxuXK/354p//+Z8zGRGVlZU8\n//zzlJeXj/wDmkBEEEIQBEGYNHyqh/WhJawPLcFyLI52n6OqPT1s46WWd1AlhQW+ClYG57JRWUEQ\n91gf8lXJKniKLDxFPR047HQHjt7hG22HdVr2ukBKd+DoHb7hDlsorpG7UEpFJOpe9uApNMldMbUK\nCoa8Nh9b083WQ27OtvT/KFUYNLlzfpzCrNG7YFYV8CsO/mt4vh0HDLePprbui4IXcl/WxUUZGK1R\nmVh7X/Bi4FCUIIo8yPCQS+peuDWHUACw5Exw43LFOl15FiUf6Ka7Ru3fSWNlAj0wOhk3SRO2HnJz\nqFbr95hfOuJiWVmSWyrjE2LIy3CJ1inEGlRw0s+Pp1hkqAgT2+nTpwdMe+GFFwZddsaMGVy4cGFI\n29mxY8eNH9wkJ4IQgiAIwqSkSArzfdOZ75vOAyUfoD7Ryjs9dSR+fOH3/FvNbylx5WeKW87zlaNI\n4799gSSDO99K3xVelMRxINkmpzMlGhS6Tmm0HUgXu9SzrUz3DXeBiea99qBEqkui/ahOrF7F6bn4\nCMxO0rjDjaRC4a2xcTEsZLTl+GzuX91Nc5dMTZuCAxQGLQomSKtJSQJdhaDbIejufV1cPXDiOJAw\n6RkeIoPqprUj0Vf34qLgRWOn3De8JCUBvVesfa12NaV/1kV2IIHXkHBSRqZAp2tdHL1WofuITtcv\nNYJzk+QuTaC6Ry7IZtvw7B7vgCATgGlL7D5jEEtKfHBxbMSOYbyINSrU73CTbOv//qgFLApujvUE\nSAVBEIZOBCEEQRCEKaHACPHB/HV8MH8dMSvBsdR5Xq/fyyste/hNw2t4FTfLA3NYmTWP5YE5+NXB\niwSON5IERsjGCCXJnpe+SExFpHRb0DqV7lqV9iM9HTj8F2VKFFhogSsXu2w9qNO0ywUX3QWON6q0\nHzJAcii7L4I6gtkWE0Gu3ybXPzECD8NBksClgUtzwGPh80HEl7rqerYD8ZSErHlpaY8NKNDZG7iI\nJRyaO226ojqxlETS7HvtKUGYocKcIwbNR3TOZTs05dkYxuULdPYr4Kk5Q75zf7ReGzQAcbEDF3SW\nlyVHNetltMWbZc7/txfHHHjiUp0KNVu9lGzqTnf2EQRBGCIRhBAEQRCmHLdicHNwCUtcM7Edm1PR\nC+nilh1H+KczP0dGYq6vvCdLYh6lrvwBY0fHK0kC3e+g+1MEZ/V04IhKmbagsXqVzhMaIKG4bTwF\nJlllEnK2jJFtZ7IaOk9pNO28wnAVRyJap+LKTY78gxImPFkCj+7g84FLuvwFa2FhurNNXV0dAJZN\n/yBFSiIekXBO6lTUKJR1KTQUWtQHJJojfYEN0756sc6+GhcOQa+E4uiZ4MWu00Nr87L3nE5h1uTN\nhmja7R40ANHLsSQad7oo/1D3KB6VIAgTnQhCCIIgCFOaLMnM8pYyy1vK/UXvpyXZwTsdR6nqOMzP\na7fx7xf+m7AeSgcksuay0DcDbbQrQN4g1ePgn25mikdaSYhd1Ba0ZoeEY/mRdSdd6LLAov3I1SuN\nt+43yJ6XZAKMYhEmqEGLdYaBGSmSnRLN77hQT+tMz7bIW9nXSSNlXRK8uLjuxUW/d8Z6inXWS0QT\nLuwhtF69WFNk8o5FSnbIRGuv/l6XaFaJNym48kQ2hCAIQzOxPkUJgiAIwgjL0YPcmbeaO/NWk7BT\nHOg6RVXHYXZ1HOJ3TW/iknWWBmani1vqy9GZeFfgig6+UhNfqQkk8Lh8NJ+JEatXidYrtLxr4Aza\n9rE/Ky4TqVHxl02dzhjC+KEHHIpujRFfmKRxkE4a2jUU6/T5fHR1RUj1dBr50Q4fcfPqAYaJkR91\nfRKtQw+wJFplEYQQBGHIRBBCEARBEC7DkDVWBCtZEazk047D2Xh9ptvGk2ef5btnf8ksT2kmS2KG\nu3jCDNu4mKyCp9DCU2iRA3SeUal7yXvV9QDM7sl7J1iYGFx5FqXD0Emjt1inrjrMyDc5VHv1IRnt\nMZn95zXmFaXQJl488oquqeCseBsQBOEaiCCEIAiCIAyBJEmUuwspdxfy0cJb6UhFOJioZkfjXp5r\neJ2f1/2BkBZgZXAuq4LzWBSYiUse2rjy8eZaik0qxtQuTCmMD5KUzu7xFkfoPKnR/I6LM89qZM1N\nkrPk2jtpLC9PXjUIIeGQ57P47wNuXj7iZmFJkqXTkuT4Jn6hUscGMyYBDlfP90gP4xIEQRgqEYQQ\nBEEQhOsQ1Hzcmb2Gdb4FpGyTw5FqqjoOs7vjCFubd6FLKov8M1mZNZcVwbnk69ljfchD5g5bqF77\nqlkOsubgK716ZwRBGC2SDMHZKfwVKdoOGrTuN+g8rhNanCB7QYKhlnMpzrJYPyvOjhOuwfeDw92L\nYiwsSdEeldh3TmdfjU5VtUF5ToplZUlm5ZvIEyxDwEo51O8zqX7TT6pLRvHYWNGrZ3fVbPGStzqO\nb5o55A4kgiBMXZLjOOIWxjjU1dWFeGrGRm8qtTj/Y0Oc/7Elzv/wOB9t4O2WA+xsPcB7HSexHJsK\nbzFrcxayNrSQykA5ymVynS9+DpJ2itea9rKjeS8RM0bYCLGpYC2LsmaN+GNoeFei5vUrX0GFl9mU\nbJhcrxXxNzC2hvv8mzGo2yXR9J6E6oaitQ4585whDzV475zEjmMStW19V9YV+Q4b59rMDF+yLwsO\n1kjsOilxrkUi4HZYWeGwosIhcIVGM+OBGYPG/RJN+yXMOGTPcihY4aD74dgvZeKtg0cW9KBD2W02\n9e/IdJ2T8BU7lG608eSP8gOYRCRJwu/3j/VhCMKIEkGIccpxnEx7qtHg8/mIRCKjtr/h2ue1bmMo\nyxcWFgKM6vkH8Rz0Eud/ZLchzv/w7nMo24iYMfZ2Hqeq4zB7Oo/RaXYTUL2sCFSyKmseSwOz8Sh9\nd1t7n4O3Tu3hf538Ec2pjgHbXOKfxV/P+Hi/9Yb7cTgONLzpouOoMfg6ZSmKbote27jxq+xzpLch\n/gcM/z4nyntQbyeNrtM6+iWdNIZyXC0Rmbgp4TNsgkMY2tHQKfPuWZ1DtTqWDbPD6eyIaSFryPsc\nihs9/6kuidaDBh3HdHCgcKlG6RqV9nhDZhkrAS3vuug4oWMn0wcvaQ6BGUlylyVQPQ6OQ7omxy4X\nyXaZwKwUuSviaN6hFwYVr/+0wsLCCVlbaCr72te+xoULF/j+978/1ocyYYjhGIIgCIIwgnyqm/Wh\nxawPLcbt9fBuw2F2dxymqv0IL7fuQUFmgb+ClcF5rAzOpZBCmuPtfOXEv9FuDv5hdV/XCb59+qf8\nr1kPjdhxSxIU3BzHX27SfkQn1qCAA0auRdbcJL4ykXYtTBy9nTRiC5M0XdxJY3Uc9xC6OlxrnYdw\nwGbTwji3VMY5eEHn3XM6P9/lI8dnsWxakgXFSXzX+2CGQahOZUEAACAASURBVLxFpvU9g67TGrLu\nEFqYIGtektKKAgDaL4oBKQbkr42TuzJOoq3nfSDL4uKSNxfX5Og4ptO8x6DrtJ/QwgShxQnkq3f8\nFYRRV15eTlNTE4qiEAgE2Lx5M0888YQIAo0CEYQQBEEQhFGiSDJzfeXM9ZXzP4o/QEOilXc6jrC7\n4wj/fuH3/KDmt5RVF+LXPJcNQPTa23mcI5Fq5vrKR/SYvSUm3hJRdE6YHNyXdtJ43oe/IknZBkbk\nU7FLgxXlSZaXJTnXqvDuWZ2Xjrh49ZiLJWUOC4tkwtfQweNGOA50nYeaXR6iNRqqzyZ/TZzg7OSQ\nggSyylUDNpIMWXOT+Gckad1v0HrAoP2YTu7yOMHZqevOnBImtn/81c1Y9sjWD1Jkjcc2v3FN60iS\nxPbt21m7di2nT59mw4YNLFy4kAcffHCEjlLoJd4KBEEQBGGMhI0Qd+ev42uzHuTni7/G4zP+B0tD\nczjcfnpI629vqRrhIxSEyaf3rn35hyMUrI8Sq1c59B8yDW+7MOMjcwdUkqAsx+LDy2I8fEsXayoS\nHK2T+NEbfv7jbS8HL2iYV0/IuC6ODV2nVc497+X4rxSsqEzh+6JU/HEX2fOHFoC4VooOeSsTTP+j\nLjxFJg1veKj+jY/uGnH/cyqy7NSofF2P3soEFRUVrFu3jn379gHw4x//mDlz5hAIBFiyZAmvvfZa\nZp3m5mY2bdpEVlYWt912G42NjZl57e3tbNq0iby8PMLhMH/5l39JKpU+ttdee41Zs/rXdJJlmdra\n2us69olMBCEEQRAEYRxwKwZrshbw+KJPYjO0cdRno3VErfgIH5kgTE6SDME5Kab/cReFaxw6j+uc\n+S8/LfsM7BFM/vG7HG6eleALd9t8ZFk3muzwwn4P/+cVP68cNWgfQjeKobBNaD+ic+ZZH7Uve5F1\nmPkhi7IPRwjMHJ2sBM3vUHRLjGn3RlB0h5otXmq2eEi0iUsQYXw5fvw4O3bsYObMmUC6Nscrr7xC\nR0cHjzzyCH/6p3+aCSY8/PDDhMNhmpqa+MY3vsHPfvazzHZs2+Yzn/kMdXV1vPfee+zZs4fvfe97\nmfmXDvWYqkM/RDhSEEZZ3DKpjnZgOQ7TPAH86pX7kAuCMLVIkoRXddNtxq667LHoef5k39+Sq2Ux\nzR1mmjtMmauAae4CSl35uJXBi0oKgtBHVqFwlYO7IkLrXoPmdw3aD6eHEARmjdzFuiLDnAKTOQUm\nLRGZved09p4z2HnaYEaeybKyJBV5JvI1XqNYCWg/bNB2SMdKSPjLUxTeEsOdZ/UURhyZx3Ml7nyL\n0g92E6lOD4Op/rWP4JwkucsTqEMo9CkII2XTpk3Ytk13dzebN2/m4YcfBuCuu+7KLPPJT36Sv/3b\nv+XEiRNUVlby3HPPcerUKTRNY+3atdx7772ZZUOhEHfffTcA4XCYT33qU2zfvp1HH3100P1P1R4R\nIgghCKOk20zxi5pDvNx8jqiVjqTqksxNOSX8ecl8fL6xLFElCMJ4clvhKn57/rWrLvdA8QfI1v5/\n9u48PqrybPz/58y+b8kkk30jhH0zArKI4oYLrV8BRer6iPrjq9Qu+u1ji5Vaa+3mY/WpfR5brSii\nCEUURUBtwQ1BFhEQEiAhC9n3zCSzn98fEwZiAiQhG3C/Xy9ewDlnzrnPmcxk5jrXfV0WilsrKfZW\nsLV+H2v9n0TXx2nspOpdpOkiAYpUnYtkfRw6hQh+CsJ3qXQycZd4sY30U7NDS8WnBur2deyk0Rdi\nTGGuHOHl0qFeDpSr2VmkZdUOIzZ9mPGpPsakBDBoTv9lJeCWqN+npeFgpNOFJduPY7QfjbV/ak6c\niSSBOSOIMdVNw7caanfraD6iwTHWh2HyQI9OuFBt2LCBSy65hHXr1vHDH/4Qt9uNw+Fg7dq1/PrX\nv6awsBBZlnG73dTW1lJdXU0oFCI5OTm6j5SUFGpqagBwu9088MAD/Otf/6KpqYlwOMzEiRMH6vQG\nLRGEEIR+4A76WXLgE462tG+155fDbK4p5pvGKv588XWYuTBTsgRBaG9+xtW8X/IpIU795cGlcXBj\n/KUoJWW75d6QnxJvJcXeSopbKyhqreST+q+prmwAQEIivi04EQlMxDOcTBxhExpRwl4Q0FjCJzpp\nbGvrpJEQxDmxa500zurYKhibEmBMcoCyRiW7izR8ckjHJ4d0DHNF2nwm2ULtAiK+OgV1e7U0HVaj\nUIN9lA/7CD8qw+C8w6pQgmO0H2t2gNrdWmp2amnKg5iL1JizAqLrjtCvjmcizJ49m3feeYcnn3yS\np59+mgULFrB27VquvvpqABITE5FlGafTiUqloqSkhNTUVABKSkrQ6/UAPPPMM9TU1PDNN99gt9t5\n8cUXefPNNwEwGo20tp7IcqysrBTTMQRB6DvLS/Z3CECcrC7g5ZkDW3l86JR+HJUgCIPVUEsaP0q/\nhWePruw0EBGjtvLLIf/RIQABoFNqyDamkG1Mabe8JeSlxFtFcWtFNHNic+0uagKNcBQUSLi0MdGM\nieMBimRdHGqF+LggXHj0zhAp13vwlKio/upEJ43YXB+aPu5oIUmQZAuRZGtl5nAve0vV7CrWsL/M\nRLwlxIQUHxnKMBV5ChoLzaiMYZyTvNiG+jlXEp2UxzNPRvip32WifLOB+v2RtqkGV98Ge4T+peyH\nAHdvHOPhhx9m4sSJ/PjHPyYQCOB0OgmHwzz//PPRTAeFQsGNN97I0qVL+etf/8ru3btZt24dN998\nMwDNzc0YDAbMZjNFRUW88MILOBwOAIYOHUp9fT2ffvopkyZN4sknnzzrMZ+rxKcKQehjLcEAm2uK\nz7jd7vpySlubSNZb+mFUgiAMdpfFTCDdkMB7VZ/zRf1eWkJenBobV8ZOZFbsJKzq7k3hMih15BhT\nyTGmtlvuCbVSQzMH6wujAYqPar+iLtAEgAIFCW3BiTS9KxqgSNTG9mpwYk/TYT6o3sqRllIkScFI\nUwbXO6cwxJh85gcLQh+RJDClBjEmu2k6pKZmp47C1Wrsw/04xvtQ6fo+28CgkZmU6Wdihp8j1SqO\nfKOldbOeioBEwChjn9RK4kj/Odv+UmMNkzU7TNWhVqq26Sh5z4QpPYBzorfPgz1C/+hu68z+8t0s\nhGHDhjFjxgz+/ve/84c//IGrr74ahULBokWLogUrAZ5//nnuvPNO4uLiyM3N5bbbbsPv9wPw0EMP\nccstt+BwOBg2bBg33XQTmzdvBsBisfDnP/+ZefPmoVKp+O1vf8sLL7zQb+c7mEjyhVoNY5CTZZny\n8vJ+O16kUFH/VirqjWN2dx9d2T4hIQGg167/nsYqHj/4aZe2vT99HNfGZ/XKcbtiMD4HvX39u0q8\nBiLE9e/bfQzEe1BXdDYud7CForYpHZHMici/G4KR7ZQoSNQ524phxkemd+jiSdDFouokQ+NUxwzJ\nYZ49upLNdbs63e5m1xXcnjSr03Xny/UH8Ro4brBf/3AQ6vdpqdsTKfrqGOvDPspHT+Jx3bl24SA0\nHVZTt1dLoFGJOi5IpTPMVx41Hr9EWkyQCak+suODKE8TjBjs11+WI+dZs0NHsFXCPsJPzHgvfVFj\ndzD+/EPkObhQU/SFC4fIhBCEPhaWux7F/7j6KK2hIKkGC2l6K7EavfhFJAjCgDCpDIw0ZTDSlNFu\neWPQ025KR3FrJe81H6Ep6AFAJSlJ0jojWRNtUzpS9S5c2hiUndyqfb1s4ykDEABvVXxMnNbONbGT\nevcEBaEHFCqIGefDmuOn9uu+76QR8kHDQS31+zSEWiVM6UESZrSijwuRCVyuV7LziI9dRRre3m3E\npA0zLsXPuFQ/5n7I0uhtkgTW7ADmjAD1e7XU7tHSeEhN7HgftuF+uhDfFAThHCCCEILQx1INVhRI\nhDnzhwFfOMRbxw7ibWtQblSqSTVYSNdbSTNYSTNYSNVbMapE8Tih/+W769jTWEVYlkkzWLjYntDp\nl0rh/GZVGRltzmK0uX3WVkPAHQ1KFLdWUOStZFdTHu5QpAiXWlKRrHNGMyaG2tOxhvWsqzxzmu6a\nis1cHTNRBGWFQUOll4m/xIv9u500JnoxJnfeSSPklfA1RN4zdTGnr3kQ8EQ6XTQe1CCHwJIdwDHa\nh8bW/saGSgkjEwOMTAxQ1aRgV7GGbYVaPj+iZWh8JDsiLSZ0zhV7VKggZnwk2FOzU0fVNh3132pw\nTvRiSuvbTiWCIPQ9EYQQhD4Wo9GTa3exvf70qY3pRhvPjrqCsCxT7WuhqLWRopYmiloa2ddczcaq\nwmggw6kxkNaWLZFqsJBmsJKkM6NWiC+EQu8raW3iz0d2cNhT3255jFrPPeljmeJIGqCRCYOJTW3C\nph7CGPOJebOyLFMfbO4wpWNH4wE8Zd4u77vMV0N+S0mHehaCMNCinTRG+aneruPYxkgnjbiJXnRt\nnTQCbomanTqaC9TIoci3Z4VaJmaEjHUM7aYa+OpP6nShAtsIH/aRXet0EWcJM2uUl8tzvOw7pmFX\nsYY3tptwGENMSPUzOtnPudYMXGWQcU1vxT7SR9U2HWUfGdG7gsRNOnF9BUE494gghCD0gztSRvFt\nUw3uUKDT9SpJ4oGhkR7CCkkiXmckXmdkoj0xuk0gHKK0tZmi1khgoqilkc01xdQGWqP7SNSZIxkT\negvphkj2hJjSIZyNCq+bX3y7haagv8O62kArfzj0JY9kTxaBCKFTkiThUFtwqC2MswyNLpdlGa8m\nxD+OvMsH1Vu7tK9yX40IQgiDlj6ufSeNorZOGrbhPsr/bSTY0v4mQTggUb1HorHIROoNHnwNCuq/\n0eIuVqMyhHHmerEO86PsQacLrRouSvczIc1PSb2SXUUa/nVQx5Z8HWNTZUYnKnBZ+7/gYygMYRnU\nPZhSoXWESbm2BU+JiqrtketrGeInNteL2nTuTTsRhAudCEIIQj9I1lt4csQM/rtgZ4e7yYk6E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Dzlz4uDcZEkKkft9D8xE1NTv1NPzThH2En5hxPpS6wVGXQ+gfoVCI66+/nvnz57NmzRr8fj+H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\n0KBBg+T3+7Vw4ULNmTMn/N4NGzb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LW9E0mQz1twf1txf1tx/nwF7U317U/9Sqg/Uqqzyq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\n9Pnnnzd776RJk5SVlaX09HRNmTJFhw8fliQVFRXJ5XL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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f671a41ba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ggplot: (-9223363281804838975)>\n"
     ]
    }
   ],
   "source": [
    "for model in models:\n",
    "   \n",
    "    print(gg.ggplot(data[data.model == model.name], gg.aes(x='err', y='time', color='method'))\n",
    "      + gg.geom_point(size=60.0)\n",
    "      + gg.geom_line()\n",
    "      + gg.scale_x_log()\n",
    "      + gg.scale_y_log()\n",
    "      + gg.xlim(1e-10, 1e-2)\n",
    "      + gg.ggtitle('Model ' + model.name)\n",
    "         )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In above plot, cvode (ADAMS or BDF) is the scikits.odes method. For the pure python case and the cython case, cvode BDF is the best performing integrator. \n",
    "Note that lsoda and odeint don't show the smooth rate as cvode does, with timings sometimes much higher for unknown reasons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
